水电系统中预报与调度的混合智能方法研究及应用
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摘要
随着水电建设的快速发展和工程规模的不断拓宽,水电系统中的问题规模和复杂度也越来越大,表现出高维、非线性、非凸等复杂特性,利用传统方法和单一智能方法对问题进行求解时结果往往不够理想,鉴于这种现状,算法混合的思想就成为提高算法性能的一个重要而有效的途径。因此根据研究问题的特点,如何有机结合各种算法的优点,构造更高效的算法,对提高水文预报的精度及水电系统经济、高效运行具有重要意义。本文首先系统介绍了概念性水文模型参数优选及不确定性分析、中长期水文预报、水电站(群)优化调度的研究现状,结合湖南双牌水库、云南澜沧江流域和贵州乌江流域的水电站(群)等工程,综合利用遗传算法、混沌系统、人工神经网络、自适应模糊推理、遗传程序设计、支持向量机等多种人工智能技术,深入研究了这些方法及其混合在概念性水文模型参数优选、概念性水文模型参数不确定性分析、中长期水文预报、水电站(群)优化调度的建模方法及应用,取得了一些有价值的研究成果。主要内容如下:
(1)以湖南省双牌水库2000-2006年水情自动测报系统积累的48场1小时洪水过程资料为基础,预报模型采用三水源新安江模型,提出三水源新安江模型参数率定的模糊多目标优化混合启发式遗传算法。该算法应用混沌变量生成初始种群,应用退化混沌变异操作代替标准的变异操作,应用SA技术提供局部邻域搜索,从实际应用出发,采用洪峰流量、峰现时间和洪水总量的合格率作为场次洪水模拟的评价目标,对三水源新安江模型参数进行模糊多目标优选,模拟和检验的比较结果表明,提出的方法能较好地获得短期洪水预报模型参数。
(2)应用概念性水文模型进行水文模拟时,由于模型本身的不足及参数多、信息量少等原因,会出现率定的最优参数组不唯一,不稳定等问题。考虑到以往的参数优选,都只得出一个参数组,无法评定优化所得参数组的不确定性的影响,存在一定的片面性和局限性,使模型的实际应用受到限制。针对这一问题。提出应用基于马尔可夫链蒙特卡罗(MCMC)理论的SCEM-UA算法,通过双牌流域不同历时的典型洪水数据对新安江模型参数进行随机优选和不确定性评估。结果表明,该算法能很好地推出新安江模型参数的后验概率分布;率定和检验结果比较分析也表明,应用SCEM-UA算法对新安江模型进行优选和不确定评估是有效和可行的。
(3)对比研究了人工神经网络(ANN)、自适应模糊推理系统(ANFIS)、遗传程序设计(GP)和支持向量机(SVM)在径流中长期中的预报建模。为了评价它们在月径流时间序列的预报效果,采用了几个标准的执行评价措施,如相关系数(R)、确定性系数(E)、均方误差(RMSE)和平均绝对百分误差(MAPE)。应用了时间序列分析建模技术ARMA作为参考的标准。通过漫湾水电站52年月径流系列及洪家渡水电站54年的月径流系列预测结果比较分析表明人工智能技术是个强有力的工具,能获得比传统时间序列分析方法更好的预报精度。分析结果也表明根据不同的统计评价指标,ANFIS、GP和SVM分别能获得最好的预报结果。这说明ANTIS、GP和SVM相对于ARMA和ANN能提高月径流时间序列预报的精度。
(4)利用混沌运动的遍历性、随机性和规律性等特点,提出了一种求解水电站水库优化调度问题的基于浮点数编码的混沌遗传(CGA)算法。该算法的思想是采用混沌优化进行改善初始种群质量和利用混沌退化变异算子代替常规算法中的变异算子,避免搜索过程陷入局部极值。利用两个著名的测试函数对提出的算法进行验证,分析结果表明提出的混沌遗传算法不但具有传统遗传算法的全局多点搜索,占用内存少等优点,而且还较好地克服了传统遗传算法的易“早熟”、易陷入局部最优和停滞的问题。通过对典型径流水电站优化调度,长系列历史径流资料的优化计算和梯级水电群的优化调度,并与传统方法相比,结果说明了该算法能提高计算结果,并具有计算速度快,搜索效率高,收敛性能好等优点。这说明提出的方法对求解复杂的水库优化调度问题是有效的和可行的。
最后对全文做了总结,并对有待于进一步研究的问题进行了展望。
With rapid development of hydropower construction and project scale widened constantly, the scale and complexity of problem is becoming bigger and bigger in hydropower system and posses properties of multi-dimension, non-linear, non-convex and etc. This is a reason that results are not ideal through the traditional method and single intelligence method to solve, and the idea of hybrid algorithms is very important way for improving effective and efficient algorithms. Therefore, how to efficiently utilize the advantages of various algorithms to develop more efficient algorithm is very significant to improve hydrological forecasting accuracy, economical and effective operation of hydropower system. In this dissertation, first, the conceptual rainfall-runoff (CRR) model parameters calibration and uncertainty analysis, the long-term forecasting and operation of hydropower system are reviewed in detail. Then, based on the project background of hydropower station(s) in Hunan Shuangpai reservoir, Yunan Lancangjiang River basin and Guizhou Wujiang River basin, the hybrid intelligence methods are studied for CRR model parameters calibration and uncertainty analysis combined with case of project application. The dissertation studies long-term forecasting and optimization operation of hydropower system using Artificial intelligence techniques such as genetic algorithms (GA), chaos system, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models, support vector machine (SVM) method and hybrid intelligence optimization methods. The major research work is outlines as follows:
(1) The hybrid metaheruistic genetic algorithm with fuzzy multiobjective optimization is presented for solving the Xinanjiang model parameters based on 48 historical floods from hydrological telemetry system with one hour routing period for 7 years (2000-2006) in Shuangpai Reservoir. The proposed method takes advantages of the ergodic and stochastic properties of chaotic variables, an annealing chaotic mutation operation which is employed to replace standard mutation operator in the evolutionary process of GA and the local optimal search capability of SA method. The three statistical ratios of acceptable criteria relative to the peak discharge, peak time and total runoff volume among the calibrated and validated historical flood events, are used to evaluate the parameter calibration performance for rainfall-runoff model by fuzzy multiobjective optimization from actual application. The results of calibration and validation indicate that the proposed method can obtain better model parameters for short-term flood forecasting.
(2) While application Xinanjiang model to simulate hydrograph, the "best" parameter set calibrated may be not unique and uncertain because of model limitation, more parameters and limited information. Considering previously parameter optimization of Xinanjiang model, there is only a unique "best" parameter set to be found and it doesn't describe uncertainty of parameter. There is a certain one-sidedness and limitation for Xinanjiang model used. Aiming at this problem, this paper presents using SCEM-UA algorithm based Markov Chain Monte Carlo (MCMC) methods for optimization and uncertainty assessment of Xinanjiang model parameters with different routing periods for Shuangpai basion. The results demonstrate that SCEM-UA algorithm is well suited to infer the posterior distribution of Xinanjiang model parameters. The results of calibration and validation indicate that it is a feasible and effective for optimization and uncertainty assessment of Xinanjiang model parameters.
(3) Developing a hydrological forecasting model to apply Artificial intelligence (AT) technology which include artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method. The main purpose is to investigate the performance of several AI methods for forecasting monthly discharge time series. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. The autoregressive moving-average (ARMA) model is also employed as reference benchmark. The results obtained in this study indicate that the AI methods are powerful tools to model the discharge time series and can give good prediction performance than traditional time series approaches through 52 years discharges series in Manwan hydropower and 54 years discharges series in Hongjiadu hydropower. The results indicate that the best performance can be obtained by ANFIS, GP and SVM in terms of different evaluation criteria. The results of the study are highly encouraging and suggest that ANFIS, GP and SVM approaches are promising in modeling monthly discharge time series comparation with the ANN and ARMA.
(4) By use of the properties of ergodicity, randomicity, and regularity of chaos, a chaos genetic algorithm (CGA) based float encoding is proposed to solve optimal operation of hydropower reservoir. CGA adopts chaos optimization of the initialization to improve species quality and utilizes annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. Comparison of results among the dynamic programming, the standard GA and CGA showed that CGA can significantly reduce the overall optimization time and improve the convergence quality through complex function optimization, hydropower station optimization operation with typical annual runoff, hydropower reservoir optimization operation with a series of monthly inflow and cascaded hydropower optimization operation. The analysis results show that CGA has obvious advantages in convergence speed and solution quality. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.
Finally, a summary is given and some problems to be further studied are discussed.
(1)以湖南省双牌水库2000-2006年水情自动测报系统积累的48场1小时洪水过程资料为基础,预报模型采用三水源新安江模型,提出三水源新安江模型参数率定的模糊多目标优化混合启发式遗传算法。该算法应用混沌变量生成初始种群,应用退化混沌变异操作代替标准的变异操作,应用SA技术提供局部邻域搜索,从实际应用出发,采用洪峰流量、峰现时间和洪水总量的合格率作为场次洪水模拟的评价目标,对三水源新安江模型参数进行模糊多目标优选,模拟和检验的比较结果表明,提出的方法能较好地获得短期洪水预报模型参数。
(2)应用概念性水文模型进行水文模拟时,由于模型本身的不足及参数多、信息量少等原因,会出现率定的最优参数组不唯一,不稳定等问题。考虑到以往的参数优选,都只得出一个参数组,无法评定优化所得参数组的不确定性的影响,存在一定的片面性和局限性,使模型的实际应用受到限制。针对这一问题。提出应用基于马尔可夫链蒙特卡罗(MCMC)理论的SCEM-UA算法,通过双牌流域不同历时的典型洪水数据对新安江模型参数进行随机优选和不确定性评估。结果表明,该算法能很好地推出新安江模型参数的后验概率分布;率定和检验结果比较分析也表明,应用SCEM-UA算法对新安江模型进行优选和不确定评估是有效和可行的。
(3)对比研究了人工神经网络(ANN)、自适应模糊推理系统(ANFIS)、遗传程序设计(GP)和支持向量机(SVM)在径流中长期中的预报建模。为了评价它们在月径流时间序列的预报效果,采用了几个标准的执行评价措施,如相关系数(R)、确定性系数(E)、均方误差(RMSE)和平均绝对百分误差(MAPE)。应用了时间序列分析建模技术ARMA作为参考的标准。通过漫湾水电站52年月径流系列及洪家渡水电站54年的月径流系列预测结果比较分析表明人工智能技术是个强有力的工具,能获得比传统时间序列分析方法更好的预报精度。分析结果也表明根据不同的统计评价指标,ANFIS、GP和SVM分别能获得最好的预报结果。这说明ANTIS、GP和SVM相对于ARMA和ANN能提高月径流时间序列预报的精度。
(4)利用混沌运动的遍历性、随机性和规律性等特点,提出了一种求解水电站水库优化调度问题的基于浮点数编码的混沌遗传(CGA)算法。该算法的思想是采用混沌优化进行改善初始种群质量和利用混沌退化变异算子代替常规算法中的变异算子,避免搜索过程陷入局部极值。利用两个著名的测试函数对提出的算法进行验证,分析结果表明提出的混沌遗传算法不但具有传统遗传算法的全局多点搜索,占用内存少等优点,而且还较好地克服了传统遗传算法的易“早熟”、易陷入局部最优和停滞的问题。通过对典型径流水电站优化调度,长系列历史径流资料的优化计算和梯级水电群的优化调度,并与传统方法相比,结果说明了该算法能提高计算结果,并具有计算速度快,搜索效率高,收敛性能好等优点。这说明提出的方法对求解复杂的水库优化调度问题是有效的和可行的。
最后对全文做了总结,并对有待于进一步研究的问题进行了展望。
With rapid development of hydropower construction and project scale widened constantly, the scale and complexity of problem is becoming bigger and bigger in hydropower system and posses properties of multi-dimension, non-linear, non-convex and etc. This is a reason that results are not ideal through the traditional method and single intelligence method to solve, and the idea of hybrid algorithms is very important way for improving effective and efficient algorithms. Therefore, how to efficiently utilize the advantages of various algorithms to develop more efficient algorithm is very significant to improve hydrological forecasting accuracy, economical and effective operation of hydropower system. In this dissertation, first, the conceptual rainfall-runoff (CRR) model parameters calibration and uncertainty analysis, the long-term forecasting and operation of hydropower system are reviewed in detail. Then, based on the project background of hydropower station(s) in Hunan Shuangpai reservoir, Yunan Lancangjiang River basin and Guizhou Wujiang River basin, the hybrid intelligence methods are studied for CRR model parameters calibration and uncertainty analysis combined with case of project application. The dissertation studies long-term forecasting and optimization operation of hydropower system using Artificial intelligence techniques such as genetic algorithms (GA), chaos system, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models, support vector machine (SVM) method and hybrid intelligence optimization methods. The major research work is outlines as follows:
(1) The hybrid metaheruistic genetic algorithm with fuzzy multiobjective optimization is presented for solving the Xinanjiang model parameters based on 48 historical floods from hydrological telemetry system with one hour routing period for 7 years (2000-2006) in Shuangpai Reservoir. The proposed method takes advantages of the ergodic and stochastic properties of chaotic variables, an annealing chaotic mutation operation which is employed to replace standard mutation operator in the evolutionary process of GA and the local optimal search capability of SA method. The three statistical ratios of acceptable criteria relative to the peak discharge, peak time and total runoff volume among the calibrated and validated historical flood events, are used to evaluate the parameter calibration performance for rainfall-runoff model by fuzzy multiobjective optimization from actual application. The results of calibration and validation indicate that the proposed method can obtain better model parameters for short-term flood forecasting.
(2) While application Xinanjiang model to simulate hydrograph, the "best" parameter set calibrated may be not unique and uncertain because of model limitation, more parameters and limited information. Considering previously parameter optimization of Xinanjiang model, there is only a unique "best" parameter set to be found and it doesn't describe uncertainty of parameter. There is a certain one-sidedness and limitation for Xinanjiang model used. Aiming at this problem, this paper presents using SCEM-UA algorithm based Markov Chain Monte Carlo (MCMC) methods for optimization and uncertainty assessment of Xinanjiang model parameters with different routing periods for Shuangpai basion. The results demonstrate that SCEM-UA algorithm is well suited to infer the posterior distribution of Xinanjiang model parameters. The results of calibration and validation indicate that it is a feasible and effective for optimization and uncertainty assessment of Xinanjiang model parameters.
(3) Developing a hydrological forecasting model to apply Artificial intelligence (AT) technology which include artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method. The main purpose is to investigate the performance of several AI methods for forecasting monthly discharge time series. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. The autoregressive moving-average (ARMA) model is also employed as reference benchmark. The results obtained in this study indicate that the AI methods are powerful tools to model the discharge time series and can give good prediction performance than traditional time series approaches through 52 years discharges series in Manwan hydropower and 54 years discharges series in Hongjiadu hydropower. The results indicate that the best performance can be obtained by ANFIS, GP and SVM in terms of different evaluation criteria. The results of the study are highly encouraging and suggest that ANFIS, GP and SVM approaches are promising in modeling monthly discharge time series comparation with the ANN and ARMA.
(4) By use of the properties of ergodicity, randomicity, and regularity of chaos, a chaos genetic algorithm (CGA) based float encoding is proposed to solve optimal operation of hydropower reservoir. CGA adopts chaos optimization of the initialization to improve species quality and utilizes annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. Comparison of results among the dynamic programming, the standard GA and CGA showed that CGA can significantly reduce the overall optimization time and improve the convergence quality through complex function optimization, hydropower station optimization operation with typical annual runoff, hydropower reservoir optimization operation with a series of monthly inflow and cascaded hydropower optimization operation. The analysis results show that CGA has obvious advantages in convergence speed and solution quality. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.
Finally, a summary is given and some problems to be further studied are discussed.
引文
[1]陆佑楣.我国水电开发与可持续发展.水力发电.2005,31(2):1-4.
[2]彭程,钱钢粮.21世纪中国水电发展前景展望.水力发电.2006,32(2):6-10.
[3]王凌,郑大钟.混合优化策略统一结构的探讨.控制与决策.2002,17(1):33-39.
[4]张洪刚,郭生练,王才君等.概念性流域水文模型参数优选技术研究.武汉大学学报(工学版).2004,37(3):18-26.
[5]Nash J E,Sutcliffe J V.River flow forecasting through conceptual models part Ⅰ—A discussion of principles.Journal of Hydrology.1970,10(3):282-290.
[6]Johnston P R,Pilgrim D H.Parameter optimization for watershed models,water Resources Research.1976,12(3):477-486.
[7]Pickup G.Testing the efficiencies of algorithms and strategies for automatic calibration of rainfall-runoff models.Hydrological science bulletin.1977,22(257-247).
[8]Gupta V K,Sorooshian S.The relationship between data and the precision of estimate parameters.Journal of Hydrology.1985,8(1):55-77.
[9]Hendrickson J D,Sorooshian S,Brazil L E.Compqrison of Newton-type and direct search algorithm for conceptual rainfall-runoff models.Water Resources Research.1988,24(5):691-700.
[10]Wang Q J.The genetic algorithm and its application to calibrating conceptual rainfall-runoff models.Water Resources Research.1991,27(9):2467-2471.
[11]毛学文.基因算法及其在水文模型参数优选中的应用.水文.1993,15(3):22-27.
[12]杨晓华,陆桂华,郦建强.混合加速遗传算法在流域模型参数优化中的应用.水科学进展.2002,13(3):340-344.
[13]谭炳卿.水文模型参数自动优选方法的比较分析.水文.1996,19(5):8-14.
[14]Franchini M.Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.1996,41(1):21-39.
[15]Franchini M,Galeati G.Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.1997,42(3):357-379.
[16]Marca F,Giorgio G.Comparing serveral genetic algorithm schemes for the calibration of conceptual rainfall-runoff models.Hydrological science Journal.1997,42(3):357-378.
[17]武新宇,程春田,赵鸣雁.基于并行遗传算法的新安江模型参数优化率定方法.水利学报.2004,35(11):85-95.
[18]陈炯烽,张万昌.基于遗传算法的新安江模型日模拟参数优选研究.水文.2006,26(4):32-38.
[19]李娜,王祥三,梅亚东等.改进遗传算法在水文模型参数优选中的应用.武汉大学学报(工学版).2007,40(5):7-14.
[20]江燕,胡铁松,桂发亮等.粒子群算法在新安江模型参数优选中的应用.武汉大学学报(工学版).2006,39(04):14-18.
[21]江燕,刘昌明,胡铁松等.新安江模型参数优选的改进粒子群算法.水利学报.2007,38(10):1200-1206.
[22]Duan Q Y,Sorooshian S,Gupta V K.Effective and efficient global optimization for conceptual rainfall-runoff models.Water Resources Research.1992,28(4):1015-1031.
[23]Duan Q,Sorooshian S,Gupta V K.Optimal use of the SCE-UA global optimization method for calibrating watershed models.Journal of Hydrology.1994,158(1):265-284.
[24]Sorooshian S,Duan Q,Gupta V K.Calibration of rainfall-runoff models:application of global optimization to the Sacramento soil moisture accounting model.Water Resources Research.1993,29(4):1185-1194.
[25]Gan T Y,Biftu G F.Automatic calibration of conceptual rainfall-runoff models:optimization algorithms,catchment conditions,and model structure.Water Resources Research.1996,32(12):3513-3524.
[26]Kuczera G.Efficient subspace probabilistic parameter optimization for catchment models.Water Resources Research.1997,33(1):177-185.
[27]Yapo P O,Gupta H V,Sorooshian S.Automatic calibration of conceptual rainfall-runoff models:Sensitivity to calibration data.Journal of Hydrology.1996,181(1-4):23-48.
[28]Luce C H,Cundy T W.Parameter identification for a runoff model for forest roads.Water Resources Research.1994,30(4):1057-1069.
[29]Cooper V A,Nguyen V T V,Nicell J A.Calibration of conceptual rainfall-runoff models using global optimisation methods with hydrologic process-based parameter constraints.Journal of Hydrology.2007,334(3-4):455-466.
[30]Goswami M,O'Connor K M.Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall-runoff model.Hydrological Sciences Journal.2007,52(3):432-449.
[31]Yapo P O,Gupta H V,Sorooshian S.Multi-objective global optimization for hydrologic models.Journal of Hydrology.1998,204(1):83-97.
[32]张洪刚,郭生练,刘攀等.概念性水文模型多目标参数自动优选方法研究.水文.2002,22(1):12-16.
[33]Yu P S,Yang T C.Fuzzy multi-objective function for rainfall-runoff model calibration.Journal of Hydrology.2000,238(1):1-14.
[34]Cheng C T,Ou C P,Chau K W.Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration.Journal of Hydrology,.2002,268(1):72-86.
[35]Cheng C T,Zhao M Y,Chau K W.Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure.Journal of Hydrology.2006,316(1-4):129-140.
[36]Khu S T,Madsen H.Multiobjective calibration with Pareto preference ordering:An application to rainfall-runoff model calibration.Water Resources Research.2005,41(3):doi:10.1029/2004WR003041.
[37]李向阳,程春田,武新宇等.水文模型模糊多目标SCE-UA参数优选方法研究.中国工程科学.2007,9(3):52-57.
[38]Beven K J.Prophecy,reality and uncertainty in distributed hydrological modeling.Advances in Water Resources.1993,16(1):41-51.
[39]Beven K J.Towards analternative blueprint for a physically based digitally simulated hydrologic response modeling system.2000,16):189-206.
[40]Beven K J,Binley A.The future of distributed models:Model calibration and uncertainty prediction.Hydrological processes.1992,6(3):279-298.
[41]lye L M.Bayes estimate of the probability of exceedence of annual floods.Stochastic Hydrology and Hydraulics.1990,4(1):56-64.
[42]Krzysztofowicz R.Bayesian theory of probabilistic via deterministic hydrologic model.Water Resources Research.1999,35(9):2739-2750.
[43]王善序.贝叶斯概率水文预报简介.水文.2001,21(5):33-34.
[44]钱名开,徐时进,王善序等.淮河息县站概率预报模型研究.水文.2004,24(2):23-25.
[45]张洪刚,郭生练,刘攀等.基于贝叶斯分析的概率洪水预报模型研究.水电能源科学.2004,22(1):22-25.
[46]李向阳,程春田.基于BP神经网络的贝叶斯概率水文预报模型.水利学报.2006,37(3):354-259
[47]Hornberger G M,Spear R C.An approach to the preliminary analysis of environmental systems.Journal of Environmental management.1981,12(1):7-18.
[48]Freer J,Beven K.Bayesian estimation of uncertainty in runoff prediction and the value of data:An application of the GLUE approach.Water Resources Research.1996,32(7):2161-2173.
[49]Cameron D S,Beven K J,J T,et al.Flood frequency estimation by continuous simulation for a gauged upland catchment(with uncertainty).Journal of Hydrology.1999,219(3-4):169-187.
[50]Franks S W,Beven K J.Bayesian estimation of uncertainty in land surface-atmosphere flux predictions.J.Geophysical Research.1997,102(D20):23991-23999.
[51]Cameron D,Beven K,Tawn J.Modelling extreme rainfalls using a modified random pulse Bartlett-Lewis stochastic rainfall model(with uncertainty).Advances in Water Resources.2000,24(2):203-211.
[52]Montanari A.Large sample behaviors of the generalized likelihood uncertainty estimation(GLUE)in assessing the uncertainty of rainfall-runoff simulations.Water Resources Research.2005,41(8):W08406.
[53]Kuczera G,Parent E.Monte Carlo assessment of parameter uncertainty in conceptual catchment models:the Metropolis algorithm.Journal of Hydrology.1998,211(1-4):69-85.
[54]Metropolis N,Rosenbluth A W,Rosenbluth M N,et al.Equation of state calculation using fast computing machines.Journal of Chemical Physics.1953,21(6):1087-1091.
[55]Campbell E P,Fox D R,Bates B C.A Bayesian approach to parameter estimation and pooling in nonlinear flood event models.Water Resources Research.1999,35(1):211-220.
[56]Bates B C,Campbell E P.A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling.Water Resources Research.2001,37(4):937-948.
[57]Marshall L,Nott D,Sharma A.A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling.Water Resources Research.2004,40(2):W02501.
[58]邢贞相.基于AM-MCMC算法的贝叶斯概率洪水预报模型.水利学报.2007,38(12).
[59]Vrugt J A,Gupta H V,Bouten W,et al.A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters.Water Resources Research.2003,39(8):18.
[60]Vrugt J A,Schoups G,Hopmans J W,et al.Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization.Water Resources Research.2004,40(6):20.
[61]Feyen L,Vrugt J A,Nuallain B O,et al.Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model.Journal of Hydrology.2007,332(3-4):276-289.
[62]中华人民共和国水利部.水文情报预报规范(SL250-2000).北京:中国水利水电出版社,2000.
[63]章淹.致洪暴雨中期预报进展.水科学进展.1995,6(2):162-168.
[64]杨旭,栾继虹.中长期水文预报研究评述与展望.西北农业大学学报.200,28(6):203-207.
[65]许士国,王富强,李红霞等.洮儿河镇西站径流长期预报研究.水文.2007,27(5):86-89.
[66]陈守煜.模糊水文学与水资源系统模糊优化原理.大连:大连理工大学出版社,1990.
[67]陈守煜,周惠成.水文点值预报与分组预报精度评价的一种数学方法.水能技术经济.1986,1):23-27.
[68]陈守煜.中长期水文预报综合分析理论模式与方法.水利学报.1997,8):15-21.
[69]白子岩.模糊模式识别法在中期预报中的应用.水利水电技术.1999,30(2):50-51.
[70]吴超羽,张文.水文预报的人工神经网络方法.中山大学学报(自然科学版).1994,33(1):79-90.
[71]Karunanithi N,Grenney W,Whitley D,et al.Neural networks for river flow prediction.Journal of Computing in Civil Engineering.1994,8(2):201-220.
[72]钟登华,王仁超,皮钧.水文预报时间序列神经网络模型.水利学报.1995,2):69-75.
[73]胡铁松,袁鹏,丁晶.人工神经网络在水文水资源中的应用.水科学进展.1995,6(1):76-82.
[74]Hsu K,Gupta H V,Sorroshian S.Artificial neural network modelling of the rainfall-runoff process.Water Resource Research.1995,31(10):2517-2530.
[75]丁晶,邓育仁,安雪松.人工神经前馈(BP)网络模型用作过渡期径流预测的探索.水电站设计.1997,13(2):69-74.
[76]邱林,陈守煜,聂相田.模糊模式识别神经网络预测模型及其应用.水科学进展.1998,9(3):258-264.
[77]许永功,李书琴,裴金萍.径流中长期预报的人工神经网络模型的建立与应用.干旱地区农业研究.2001,19(3):104-108.
[78]程春田,孙英广.BP神经网络模型结构对漫湾径流预报精度的影响研究.水电能源科学.2005,23(2):4-7.
[79]Huang W,Xu B,Chan-Hilton A.Forecasting flows in apalachicola river using neural networks.Hydrological Processes.2004,18(13):2545-2564.
[80]徐留兴,梁川,秦远清.改进的Elman模型在紫坪铺月径流预测中的应用.四川大学学报(工程科学版).2006,38(3):38-42.
[81]屈亚玲,周建中,刘芳等.基于改进的Elman神经网络的径流中长期预报.水文.2006,26(1):45-50.
[82]Sohail A,Watanabe K,Takeuchi S.Runoff analysis for a small watershed of Tono area Japan by back propagation artificial neural network with seasonal data.Water Resources Management.2008,22(1):1-22.
[83]夏军.中长期径流预估的一种灰关联模式与预测方法.水科学进展.1993,4(3):190-197.
[84]冯平,杨鹏,李润苗.枯水期径流量的中长期预报模式.水利水电技术.1992,2):6-9.
[85]袁秀娟,夏军.径流中长期预报的灰色系统方法研究.武汉水力电力大学.1994,27(4):367-375.
[86]陈意平,李小牛.灰色系统理论在水利中的应用及前景人民珠江.1996,1):25-27.
[87]赵璀.灰色系统在瑞丽江长期水文预报中的应用.云南水力发电.2007,23(6):5-8.
[88]谢景新.非线性多步预测与优化方法及其在水文预报中的应用:(博士学位论文).大连:大连理工大学,2006.
[89]Breaford P W,Seyfried M S,Matison T H.Searching for chaotic dynamic in snowmelt runoff.Water Resources Research.1991,27(6):1005-1010.
[90]Jayawardena A W,Lai F.Analysis and prediction of chaos in rainfall and stream flow time series.Journal of Hydrology.1994,153(1-4):23-52.
[91]Sivakumar B,Berndtsson R,Persson M.Monthly runoff prediction using phase-space reconstruction.Hydrological Sciences Journal,2001,46(3):377-388.
[92]赵永龙,丁晶,邓育仁.相空间小波网络模型及其在水文中长期预测中的应用.水科学进展.1998,9(3):252-257.
[93]权先璋,蒋传文,张勇传.径流预报的混沌神经网络理论及应用.武汉城市建设学院学报.1999,16(3):33-37.
[94]丁涛,周惠成,黄建辉.混沌水文时间序列区间预测研究.水利学报.2004,34(12):15-21.
[95]陈南祥,黄强,曹连海等.径流序列的相空间重构神经网络预测模型.河海大学学报(自然科学版).2005,33(5):490-493.
[96]李红霞,许士国.月径流序列的混沌特征识别及Volterra自适应预测法的应用.水利学报.2007,38(6):760-766.
[97]Chui C K.小波分析导论.西安:西安交通大学出版社,1995.
[98]Cao L Y.Predicting chaotic time series with wavelet network Physica D.1995,85(1):225-238.
[99]尤卫红,杞明辉,段旭.小波变换在短期气候预测模型.高原气象.1999,18(1):39-46.
[100]李贤彬,丁晶,李后强等.子波分析及其在水文水资源中潜在应用.四川联合大学学报(工程科学版).1997,1(4):49-52.
[101]周惠成,彭勇.基于小波分解的月径流预测校正模型研究.系统仿真学报.2007,19(5):1104-1108.
[102]Koza J.Genetic Programming:On the Programming of Computers by Natural Selection.Cambridge,MA.:MIT Press,1992.
[103]Khu S T,Liong S Y,Babovic V.Genetic Programming and Its Application in Real-Time Runoff Forecasting.Journal of the American Water Resources Association.2001,37(2):439-451.
[104]Liong S Y,Gautam R R,Khu S T,et al.Genetic Programming:A new paradigm in rainfall runoff modeling.Journal of the American Water Resources Association.2002,38(3):705-718.
[105]王文川,程春田.基于遗传程序设计的中长期径流预报模型研究及应用.水电能源科学.2006,24(6):29-32.
[106]袁晓辉,张勇传,袁艳斌.基于遗传规划的径流预测新方法.水力发电.2006,32(8):11-13.
[107]Vapnik V.The Nature of Statistical Learning Theory.New York:Springer,1995.
[108]廖杰,王文圣,李跃清.支持向量机及其在径流预测中的应用.四川大学学报(工程科学版).2006,38(6):24-29.
[109]林剑艺,程春田.支持向量机在中长期径流预报中的应用.水利学报.2006,37(6):681-686.
[110]Yu P S,Chen S T,Chang I F.Support vector regression for real-time flood stage forecasting.Journal of Hydrology.2006,328(3-4):704-716.
[111]Khan M S,Coulibaly P.Application of support vector machine in lake water level prediction.Journal of Hydrologic Engineering.2006,11(3):199-205.
[112]Little J D C.The Use of Storage Water in a Hydroelectric System.Journal of the Operations Research Society of America.1955,3(2):187-197
[113]Yeh W G.Reservoir management and operation models:A state-of-the-art review,water Resources Research.1985,21(12):1797-1818.
[114]Simonovic S P.Reservoir systems analysis:closing gap between theory and practice.Journal of Water Resources Planning and Management.1992,118(3):262-280.
[115]Windsor J S.Optimization model for reservoir flood control.water Resources Research.1973,9(5):1103-1114.
[116]伍宏中.水电站群补偿径流调节的线性规划模型及其应用.水力发电学报.1998,60(1):10-22.
[117]Needham J,Watkins D,Lund J.Linear programming for flood control in the Iowa and Des Moines rivers.Journal of water Resources Planning and Management.2000,126(3):118-127.
[118]Peng C,Buras N.Dynamic operation of a surface water resources system.Water Resources Research.2000,36(9):2701-2709.
[119]倪二男,管晓宏,李仁厚.梯级水电系统组合优化调度算法研究.中国电机工程学报.1999,19(1):19-23.
[120]Barros M T L,Tsai F T C,Yang S L.Optimization of large-scale hydro power system operations.Journal of Water Resources Planning and Management.2003,129(3):178-188.
[121]Brannlund H,Sjelvgren D.Short term generations cheduling with security constraints.IEEE Trans on Power Systems.1988,3(1):310-316.
[122]朱继忠,徐国禹.用网流法求解水火电力系统有功负荷分配.系统工程理论与实践.1995,15(1):68-73.
[123]杨侃,张静怡,董增川.长江防洪系统网络分析分解协调优化调度研究.河海大学学报(自然科学版).2000,28(3):77-81.
[124]罗强,宋朝红,雷声隆.水库群系统非线性网络流规划法.武汉大学学报(工学版).2001,34(3):22-26.
[125]叶秉如.水资源系统优化规划和调度.北京:中国水利水电出版社,1999.
[126]Young G K.Finding Reservoir operating Rules.Hydraulic Am.Civ.Eng.1967,93(HY6):297-321.
[127]谭维炎,黄守信.应用随机动态规划进行水电站的优化调度.水利学报.1982,7(1-7.
[128]Labadie J W.Optimal operation of multireservoir systems:State-of-the-art review.Journal of water Resources Planning and Management.2004,130(2):93-111.
[129]倪建军,徐立中,李臣明等.水库调度决策研究综述.水利水电科技进展.2004,24(6):63-66.
[130]Fred N L.A Method to Eliminate solution Trapping In Applying Progressive optimality principle to short-term hydrothermal scheduling.IEEE Trans on Power Systems.1989,4(3):935-942.
[131]宗航,李承军,周建中等.POA算法在梯级水电站短期优化调度中的应用.水电能源科学.2003,21(1):45-47.
[132]马光文,王黎.遗传算法在水电站优化调度中的应用.水科学进展.1997,8(3):275-280.
[133]伍永刚,王定一.二倍体遗传算法求解梯级水电站日优化调度问题.水电能源科学.1999,17(3):31-34.
[134]王大刚,程春田,李敏.基于遗传算法的水电站优化调度研究.华北水利水电学院学报.2001,22(1):5-10.
[135]畅建霞,黄强,王义民.基于改进遗传算法的水电站水库优化调度.水力发电学报.2001,3):85-90.
[136]Wu Y G,Ho C Y,Wang D.A diploid genetic approach to short-term scheduling of hydro-thermal system.IEEE Trans.on Power Systems.2000,15(4):1268-1274.
[137]Oliveira R,Haunert D.Operating rules for multireservoir systems.Water Resources Research.1997,33(4):839-852.
[138]Chang F,Chen L,Chang L C.Optimizing the reservoir operating rule curves by genetic algorithms.Hydrological Processes.2005,19(11):2277-2289.
[139]钟登华,熊开智,成立芹.遗传算法的改进及其在水库优化调度中的应用研究.中国工程科学.2003,5(9):22-26.
[140]Ahmed J A,Sarma A K.Genetic Algorithm for optimal operating policy of a multipurpose reservoir.Water Resources Management.2005,19(2):145-161.
[141]王万良,周慕逊,管秋等.基于遗传算法的小水电站优化调度方法的研究与实践.水力发电学报.2005.24(3):6-11.
[142]Jothiprakash V,Shanthi G.Single reservoir operating policies using genetic algorithm.Water Resources Management.2006,20(6):917-929.
[143]胡铁松.水库调度智能决策支持系统理论与应用研究:(博士学位论文).武汉:武汉水利电力大学,1993.
[144]Park J H,Kim Y S,Lee K Y.Economic load dispatch for piecewise quadratic cost function using Hopfield neural network.IEEE Transactions on Power Systems.1993,8(3):1030-1038.
[145]Naresh R,Sharma J.Short term hydro scheduling using two-phase neural network.Electrical Power & Energy Systems.2002,24(6):583-590.
[146]Wong K P,Wong Y W.Short-term hydrothermal scheduling part 1:simulated annealing approach.IEE Proceedings-Generation,Transmission and Distribution.1994,141(5):497-501.
[147]Wong K P,Wong Y W.Short-term hydrothermal scheduling part 2:parallel simulated annealing approach.IEE Proceedings-Generation,Transmission and Distribution.1994,141(5):502-506.
[148]张双虎,黄强,孙廷容.基于并行组合模拟退火算法的水电站优化调度研究.水力发电学报.2004,23(4):16-19.
[149]袁晓辉,袁艳斌,权先璋等.基于混沌进化算法的梯级水电系统短期发电计划.电力系统自动化.2001,16(34-38.
[150]邱林,田景环,段春青等.混沌优化算法在水库优化调度中的应用.中国农村水利水电.2005,7(17-20.
[151]Dorigo M,Maniezzo V,Colorni A.The ant system:Optimization by a colony of cooperating agents.IEEE Transactions on Systems,Man,and Cybernetics.1996,1(26):29-41.
[152]Huang S J.Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches.IEEE Trans.on Power Systems.2001,16(3):296-301.
[153]徐刚,马光文,涂扬举.蚁群算法求解梯级水电厂日竞价优化调度问题.水利学报.2005,36(8):978-987.
[154]徐刚,马光文,梁武湖等.蚁群算法在水库优化调度中的应用.水科学进展.2005,16(3):397-400.
[155]王德智,董增川,丁胜祥.基于连续蚁群算法的供水水库优化调度.水电能源科学.2006,24(2):77-79.
[156]胡国强,贺仁睦.基于自适应蚁群算法的水电站水库优化调度.中国电力.2007,40(7):48-50.
[157]kenndey J,Eberhart R.Particle Swarm Optimization.IEEE Int'1 conf on Neural Networks,Perth,Australia,1995.
[158]武新宇,程春田,廖胜利.两阶段粒子群算法在水电站群优化调度中的应用.电网技术.2006,30(20):25-28.
[159]杨道辉,马光文,过夏明等.粒子群算法在水电站优化调度中的应用.水力发电学报.2006,25(5):5-7.
[160]张双虎,黄强,吴洪寿等。水电站水库优化调度的改进粒子群算法.水力发电学报.2007,26(1):1-5.
[161]陈功贵,杨俊杰,高仕红.基于混合PSO算法的梯级水库优化调度研究.水力发电.2007,33(10):86-88.
[162]Crawford N H,Linsley R K.Digital simulation in Hydrology:Stanford Watershed Model Ⅳ:Technical Report 39,Dept.of Civil Engineering,Stanmford Universitys,1966.
[163]Rockwood D M.Theory and practice of the SSARR model as related to analyzing and forecasting the response of hydrologic systems.In V.P,Singh,ed.Applied modeling in catchment hydrology.Littleton,Colo:Water Resources Publications,1982:87-106.
[164]Brazil L E,Hudlow M D.Calibration procedures used with the national weather service forecast system.In Y Y Haimes and J Kindler,ed.Water and Related Land Resources.Pergamon,New York,1981:457-466.
[165]Sugawara M.Tank model.In V.P.Singh,ed.Computer models of watershed hydrology.Littleton,Colo:Water Resources Publications,1995.
[166]赵人俊.流域水文模拟.北京:水利水电出版社,1984.
[167]Duan Q Y,Sorooshian S,Gupta V.Effective and Efficient Global Optimization for Conceptual Rainfall-runoff Models.Water Resources Research.1992,28(4):1015-1031.
[168]Cooper V A,Nguyen V T V,Nicell J A.Evaluation of global optimization methods for conceptual rainfall-runoff model calibration.Water Science and Technology.1997,36(5):53-60.
[169]Ndiritu J G,Daniell T M.An improved genetic algorithm for rainfall-runoff model calibration and function optimization.Mathematical and Computer Modelling.2001,33(6-7):695-706.
[170]陆桂华,郦建强,杨晓华.水文模型参数优选遗传算法的应用.水利学报.2004,02):50-56.
[171]Cheng C T,Ou C P,Chau K W.Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration.Journal of Hydrology.2002,268(1-4):72-86.
[172]Cheng C-T,Zhao M-Y,Chau K W,et al.Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure.Journal of Hydrology.2006,316(1-4):129-140.
[173]Zhao R-J.The Xinanjiang model applied in China.Journal of Hydrology.1992,135(1-4):371-381.
[174]Goldberg D E.Genetic algorithm in Search,Optimization and Machine Learning.Inc.,Reading,MA.:Addison-Wesley Publishing Co.,1989.
[175]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社,1999.
[176]李兵,蒋慰孙.混沌优化方法及其应用.控制理论与应用.1997,14(4):613-615.
[177]May R M.Simple mathematical models with very complicated dynamics.Nature.1976,261(459-467.
[178]康立山.非数值并行算法(第一册)-模拟退火算法.北京:科学出版社,1997.
[179]Laarhoven P J M V,Aarts E H L.Simulated Annealing:Theory and Applications.Dordrecht,The Netherlands:Kluwer,1987.
[180]Janikow C Z,Michalewicz Z.An experimental comparison of binary and floating point representation in genetic algorithms.Proceedings of the Fourth International Conference on Genetic Algorithms,San Francisco,1991:31-36.
[181]李耀华,徐乐江,胡国奋等.基于混沌遗传算法的板坯入库决策优化方法.系统仿真学报.2005,17(11):2620-2624.
[182]Boyle D P,Gupta H V,Sorooshian S.Toward improved calibration of hydrologic models:Combining the strengths of manual and automatic methods.Water Resources Research.2000,36(12):3663-3674.
[183]陈守煜.系统模糊决策理论与应用.大连:大连理工大学出版社,1994.
[184]长江水利委员会水文局,葛守西.现代洪水预报技术.北京:中国水利水电出版社,1999.
[185]Thiemann M,Trosset M,Gupta H.Bayesian recursive parameter estimation for hydrologic models.Water Resources Research.2001,37(10):2521-2535.
[186]Bates B C,Campbell E P.A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling.Water Resources Research.2001,37(4).
[187]Vrugt J A,Gupta H V,Dekker S C,et al.Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting Model.Journal of Hydrology.2006,325(1-4):288-307.
[188]Hapualrachchi H A P,Li Z-j,Wang S.Application of SCE-UA method for calibrating the Xin'anjiang watershed model.Journal of Lake Sciences.2001,12(4):304-314.
[189]刘力,周建中,杨俊杰等.基于改进粒子群优化算法的新安江模型参数优选.水力发电.2007,33(7):16-19.
[190]Sorooshian S,Duan Q Y,Gupta V K.Calibration of rainfall-runoff model:application of global optimization to the sacramento soil moisture accornting model.Water Resources Research.1993,29(4):1185-1194.
[191]Duan Q,Sorooshian S,Gupta V K.Optimal use of the SCE-UA global optimization method for calibrating watershed models.Journal of Hydrology.1994,158(3):265-284.
[192]Thyer M,Kuczera G,Bates B C.Probabilistic optimization for conceptual rainfall-runoff models:A comparison of the shuffled complex evolution and simulated annealing algorithms.Water Resources Research.1999,35(3):767-773.
[193]Franchini M,Galeati G,Berra S.Global optimization techniques for the calibration of conceptual rainfall-runoff models.Hydrological Sciences Journal.1998,43(3):443-458.
[194]Box G E P,Tiao G C.Bayesian infererce in Statistical Analyses.Addison-Wesley-Longman,Reading,Mass.,1973.
[195]Chen H L,Rao A R.Testing hydrologic time series for stationarity.Journal of Hydrologic Engineering.2002,7(2):129-136.
[196]Salas J D.Analysis and modeling of hydrologic time series.1993.
[197]Srikanthan R,McMahon T A.Stochastic generation of annual,monthly and daily climate data:A review.Hydrology and Earth System Sciences.2001,5(4):653-670.
[198]Committee A T.Artificial neural networks in hydrology-Ⅰ:Preliminary concepts.Journal of Hydrologic Engineering-ASCE.2000,5(2):115-123.
[199]Committee A T.Artificial neural networks in hydrology-Ⅱ:Hydrological applications.Journal of Hydrologic Engineering-ASCE.2000,5(2):124-137.
[200]Jang J-S R.ANFIS:Adaptive-Network-based Fuzzy Inference Systems.IEEE Transactions on Systems,Man,and Cybernetics.1993,23(3):665-685.
[201]Bazartseren B,Hildebrandt G,Holz K P.Short-term water level prediction using neural networks and neuro-fuzzy approach.Neurocomputing.2003,55(3-4):439-450.
[202]Nayak P C,Sudheer K P,Rangan D M,et al.A neuro-fuzzy computing technique for modeling hydrological time series.Journal of Hydrology.2004,291(1-2):52-66.
[203]geskin M E,Taylan D,Terzi O.Adaptive neural-based fuzzy inference system(ANFIS)approach for modelling hydrological time series.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.2006,51(4):588-598.
[204]马细霞,陈鑫,胡铁成.基于ANFIS模型的年径流预报方法.郑州大学学报(工学版).2007,28(3):111-124.
[205]林剑艺,程春田,陈艳.基于自适应模糊推理系统模型的径流中长期预报.水电能源科学.2005,23(6):25-27.
[206]Johari A,Habibagahi G,Ghahramani A.Prediction of soil-water characteristic curve using genetic programming.Journal of Geotechnical and Geoenvironmental Engineering.2006,132(5):661-665.
[207]Vapnik V.Statistical learning theory.New York:Wiley,1998.
[208]Chau K W,Cheng C T.Real-time prediction of water stage with artificial neural network approach.Lecture Notes in Artificial Intelligence.2002,2557(715.
[209]Haykin S.Neural networks:a comprehensive foundation.New Jersey:Upper Saddle River,1999.
[210]Jang J-S R,Sun C-T,Mizutani E.Neuro-Fuzzy and Soft Computing:A Computational Approach to Learning and Machine Intelligence.Upper Saddle River,NJ.:Prentice-Hall,1997.
[211]Koza J R,Bennett H B,DAndre,et al.Genetic Programming Ⅲ:Darwinian Invention and Problem Solving.San Francisco:CA:Morgan Kaufmann Publishers,1999.
[212]Sudheer K P,Gosain A K,Ramasastri K S.A data-driven algorithm for constructing artificial neural network rainfall-runoff models.Hydrological Processes.2002,16(6):1325-1330.
[213]Lin J Y,Cheng C T,Chau K W.Using support vector machines for long-term discharge prediction.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.2006,51(4):599-612.
[214]Santhi C,Arnold J G,Williams J R,et al.Validation of tbe swat model on a large river basin with point and nonpoint sources.Journal of the American Water Resources Association.2001,37(5):1169-1188.
[215]Yan Liew M W,Arnold J G,Garbrecht J D.Hydrologic simulation on agricultural watersheds:Choosing between two models.Transactions of the Asae.2003,46(6):1539-1551.
[216]Moriasi D N,Arnold J G,Van Liew M W,et al.Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Transactions of the ASABE.2007,50(3):885-900.
[217]Moiler M F.A scaled conjugate gradient algorithm for fast supervised learning.Neural Networks.1993,6(4):525-533.
[218]Liong S Y,Sivapragasam C.Flood stage forecasting with support vector machines.Journal of the American Water Resources Association.2002,38(1):173-186.
[219]Pai P-F,nong W-C.Support vector machines with simulated annealing algorithms in electricity load forecasting.Energy Conversion and Management.2005,46(17):2669-2688.
[220]Pai P-F,Hong W-C.Software reliability forecasting by support vector machines with simulated annealing algorithms.Journal of Systems and Software.2006,79(6):747-755.
[221]Yu X Y,Liong S Y,Babovic V.EC-SVM approach for real-time hydrologic forecasting.Journal of Hydroinformatics.2004,6(3):209-223.
[222]Duan Q Y,Gupta V K,SorooshianS.Shuffled complex evolution approach for effective and efficient minimization.Journal of Optimization Theory and Applications.1993,76(3):501-521.
[223]张亚乐,徐博文,一种改进的遗传算法在原油蒸馏过程优化中的应用.化工自动化及仪表.1997,24(3):12-17.
[224]唐巍,郭镇明,唐嘉亨等.复杂函数优化的混沌遗传算法.哈尔滨工程大学学报.2000,21(5):1-5.
[225]LU Q-z,Shen G-l,Yu R-q.A chaotic approach to maintain the population diversity of genetic algorithm in network training,Computational Biology and Chemistry.2003,27(3):363-371.
[226]Rosenbrock H H.An Automatic Method for Finding the Greatest or Least Value of a Function.Computer Journal.1960,3:175-184.
[227]Siarry P,Petrowski A,Bessaou M.A multipopulation genetic algorithm aimed at multimodal optimization.Advances in Engineering Software.2002,33(4):207-213.
[228]Wei G.Fast immunized evolutionary programming.Evolutionary Computation 2004,Congress,2004:666-670.
[2]彭程,钱钢粮.21世纪中国水电发展前景展望.水力发电.2006,32(2):6-10.
[3]王凌,郑大钟.混合优化策略统一结构的探讨.控制与决策.2002,17(1):33-39.
[4]张洪刚,郭生练,王才君等.概念性流域水文模型参数优选技术研究.武汉大学学报(工学版).2004,37(3):18-26.
[5]Nash J E,Sutcliffe J V.River flow forecasting through conceptual models part Ⅰ—A discussion of principles.Journal of Hydrology.1970,10(3):282-290.
[6]Johnston P R,Pilgrim D H.Parameter optimization for watershed models,water Resources Research.1976,12(3):477-486.
[7]Pickup G.Testing the efficiencies of algorithms and strategies for automatic calibration of rainfall-runoff models.Hydrological science bulletin.1977,22(257-247).
[8]Gupta V K,Sorooshian S.The relationship between data and the precision of estimate parameters.Journal of Hydrology.1985,8(1):55-77.
[9]Hendrickson J D,Sorooshian S,Brazil L E.Compqrison of Newton-type and direct search algorithm for conceptual rainfall-runoff models.Water Resources Research.1988,24(5):691-700.
[10]Wang Q J.The genetic algorithm and its application to calibrating conceptual rainfall-runoff models.Water Resources Research.1991,27(9):2467-2471.
[11]毛学文.基因算法及其在水文模型参数优选中的应用.水文.1993,15(3):22-27.
[12]杨晓华,陆桂华,郦建强.混合加速遗传算法在流域模型参数优化中的应用.水科学进展.2002,13(3):340-344.
[13]谭炳卿.水文模型参数自动优选方法的比较分析.水文.1996,19(5):8-14.
[14]Franchini M.Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.1996,41(1):21-39.
[15]Franchini M,Galeati G.Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.1997,42(3):357-379.
[16]Marca F,Giorgio G.Comparing serveral genetic algorithm schemes for the calibration of conceptual rainfall-runoff models.Hydrological science Journal.1997,42(3):357-378.
[17]武新宇,程春田,赵鸣雁.基于并行遗传算法的新安江模型参数优化率定方法.水利学报.2004,35(11):85-95.
[18]陈炯烽,张万昌.基于遗传算法的新安江模型日模拟参数优选研究.水文.2006,26(4):32-38.
[19]李娜,王祥三,梅亚东等.改进遗传算法在水文模型参数优选中的应用.武汉大学学报(工学版).2007,40(5):7-14.
[20]江燕,胡铁松,桂发亮等.粒子群算法在新安江模型参数优选中的应用.武汉大学学报(工学版).2006,39(04):14-18.
[21]江燕,刘昌明,胡铁松等.新安江模型参数优选的改进粒子群算法.水利学报.2007,38(10):1200-1206.
[22]Duan Q Y,Sorooshian S,Gupta V K.Effective and efficient global optimization for conceptual rainfall-runoff models.Water Resources Research.1992,28(4):1015-1031.
[23]Duan Q,Sorooshian S,Gupta V K.Optimal use of the SCE-UA global optimization method for calibrating watershed models.Journal of Hydrology.1994,158(1):265-284.
[24]Sorooshian S,Duan Q,Gupta V K.Calibration of rainfall-runoff models:application of global optimization to the Sacramento soil moisture accounting model.Water Resources Research.1993,29(4):1185-1194.
[25]Gan T Y,Biftu G F.Automatic calibration of conceptual rainfall-runoff models:optimization algorithms,catchment conditions,and model structure.Water Resources Research.1996,32(12):3513-3524.
[26]Kuczera G.Efficient subspace probabilistic parameter optimization for catchment models.Water Resources Research.1997,33(1):177-185.
[27]Yapo P O,Gupta H V,Sorooshian S.Automatic calibration of conceptual rainfall-runoff models:Sensitivity to calibration data.Journal of Hydrology.1996,181(1-4):23-48.
[28]Luce C H,Cundy T W.Parameter identification for a runoff model for forest roads.Water Resources Research.1994,30(4):1057-1069.
[29]Cooper V A,Nguyen V T V,Nicell J A.Calibration of conceptual rainfall-runoff models using global optimisation methods with hydrologic process-based parameter constraints.Journal of Hydrology.2007,334(3-4):455-466.
[30]Goswami M,O'Connor K M.Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall-runoff model.Hydrological Sciences Journal.2007,52(3):432-449.
[31]Yapo P O,Gupta H V,Sorooshian S.Multi-objective global optimization for hydrologic models.Journal of Hydrology.1998,204(1):83-97.
[32]张洪刚,郭生练,刘攀等.概念性水文模型多目标参数自动优选方法研究.水文.2002,22(1):12-16.
[33]Yu P S,Yang T C.Fuzzy multi-objective function for rainfall-runoff model calibration.Journal of Hydrology.2000,238(1):1-14.
[34]Cheng C T,Ou C P,Chau K W.Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration.Journal of Hydrology,.2002,268(1):72-86.
[35]Cheng C T,Zhao M Y,Chau K W.Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure.Journal of Hydrology.2006,316(1-4):129-140.
[36]Khu S T,Madsen H.Multiobjective calibration with Pareto preference ordering:An application to rainfall-runoff model calibration.Water Resources Research.2005,41(3):doi:10.1029/2004WR003041.
[37]李向阳,程春田,武新宇等.水文模型模糊多目标SCE-UA参数优选方法研究.中国工程科学.2007,9(3):52-57.
[38]Beven K J.Prophecy,reality and uncertainty in distributed hydrological modeling.Advances in Water Resources.1993,16(1):41-51.
[39]Beven K J.Towards analternative blueprint for a physically based digitally simulated hydrologic response modeling system.2000,16):189-206.
[40]Beven K J,Binley A.The future of distributed models:Model calibration and uncertainty prediction.Hydrological processes.1992,6(3):279-298.
[41]lye L M.Bayes estimate of the probability of exceedence of annual floods.Stochastic Hydrology and Hydraulics.1990,4(1):56-64.
[42]Krzysztofowicz R.Bayesian theory of probabilistic via deterministic hydrologic model.Water Resources Research.1999,35(9):2739-2750.
[43]王善序.贝叶斯概率水文预报简介.水文.2001,21(5):33-34.
[44]钱名开,徐时进,王善序等.淮河息县站概率预报模型研究.水文.2004,24(2):23-25.
[45]张洪刚,郭生练,刘攀等.基于贝叶斯分析的概率洪水预报模型研究.水电能源科学.2004,22(1):22-25.
[46]李向阳,程春田.基于BP神经网络的贝叶斯概率水文预报模型.水利学报.2006,37(3):354-259
[47]Hornberger G M,Spear R C.An approach to the preliminary analysis of environmental systems.Journal of Environmental management.1981,12(1):7-18.
[48]Freer J,Beven K.Bayesian estimation of uncertainty in runoff prediction and the value of data:An application of the GLUE approach.Water Resources Research.1996,32(7):2161-2173.
[49]Cameron D S,Beven K J,J T,et al.Flood frequency estimation by continuous simulation for a gauged upland catchment(with uncertainty).Journal of Hydrology.1999,219(3-4):169-187.
[50]Franks S W,Beven K J.Bayesian estimation of uncertainty in land surface-atmosphere flux predictions.J.Geophysical Research.1997,102(D20):23991-23999.
[51]Cameron D,Beven K,Tawn J.Modelling extreme rainfalls using a modified random pulse Bartlett-Lewis stochastic rainfall model(with uncertainty).Advances in Water Resources.2000,24(2):203-211.
[52]Montanari A.Large sample behaviors of the generalized likelihood uncertainty estimation(GLUE)in assessing the uncertainty of rainfall-runoff simulations.Water Resources Research.2005,41(8):W08406.
[53]Kuczera G,Parent E.Monte Carlo assessment of parameter uncertainty in conceptual catchment models:the Metropolis algorithm.Journal of Hydrology.1998,211(1-4):69-85.
[54]Metropolis N,Rosenbluth A W,Rosenbluth M N,et al.Equation of state calculation using fast computing machines.Journal of Chemical Physics.1953,21(6):1087-1091.
[55]Campbell E P,Fox D R,Bates B C.A Bayesian approach to parameter estimation and pooling in nonlinear flood event models.Water Resources Research.1999,35(1):211-220.
[56]Bates B C,Campbell E P.A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling.Water Resources Research.2001,37(4):937-948.
[57]Marshall L,Nott D,Sharma A.A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling.Water Resources Research.2004,40(2):W02501.
[58]邢贞相.基于AM-MCMC算法的贝叶斯概率洪水预报模型.水利学报.2007,38(12).
[59]Vrugt J A,Gupta H V,Bouten W,et al.A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters.Water Resources Research.2003,39(8):18.
[60]Vrugt J A,Schoups G,Hopmans J W,et al.Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization.Water Resources Research.2004,40(6):20.
[61]Feyen L,Vrugt J A,Nuallain B O,et al.Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model.Journal of Hydrology.2007,332(3-4):276-289.
[62]中华人民共和国水利部.水文情报预报规范(SL250-2000).北京:中国水利水电出版社,2000.
[63]章淹.致洪暴雨中期预报进展.水科学进展.1995,6(2):162-168.
[64]杨旭,栾继虹.中长期水文预报研究评述与展望.西北农业大学学报.200,28(6):203-207.
[65]许士国,王富强,李红霞等.洮儿河镇西站径流长期预报研究.水文.2007,27(5):86-89.
[66]陈守煜.模糊水文学与水资源系统模糊优化原理.大连:大连理工大学出版社,1990.
[67]陈守煜,周惠成.水文点值预报与分组预报精度评价的一种数学方法.水能技术经济.1986,1):23-27.
[68]陈守煜.中长期水文预报综合分析理论模式与方法.水利学报.1997,8):15-21.
[69]白子岩.模糊模式识别法在中期预报中的应用.水利水电技术.1999,30(2):50-51.
[70]吴超羽,张文.水文预报的人工神经网络方法.中山大学学报(自然科学版).1994,33(1):79-90.
[71]Karunanithi N,Grenney W,Whitley D,et al.Neural networks for river flow prediction.Journal of Computing in Civil Engineering.1994,8(2):201-220.
[72]钟登华,王仁超,皮钧.水文预报时间序列神经网络模型.水利学报.1995,2):69-75.
[73]胡铁松,袁鹏,丁晶.人工神经网络在水文水资源中的应用.水科学进展.1995,6(1):76-82.
[74]Hsu K,Gupta H V,Sorroshian S.Artificial neural network modelling of the rainfall-runoff process.Water Resource Research.1995,31(10):2517-2530.
[75]丁晶,邓育仁,安雪松.人工神经前馈(BP)网络模型用作过渡期径流预测的探索.水电站设计.1997,13(2):69-74.
[76]邱林,陈守煜,聂相田.模糊模式识别神经网络预测模型及其应用.水科学进展.1998,9(3):258-264.
[77]许永功,李书琴,裴金萍.径流中长期预报的人工神经网络模型的建立与应用.干旱地区农业研究.2001,19(3):104-108.
[78]程春田,孙英广.BP神经网络模型结构对漫湾径流预报精度的影响研究.水电能源科学.2005,23(2):4-7.
[79]Huang W,Xu B,Chan-Hilton A.Forecasting flows in apalachicola river using neural networks.Hydrological Processes.2004,18(13):2545-2564.
[80]徐留兴,梁川,秦远清.改进的Elman模型在紫坪铺月径流预测中的应用.四川大学学报(工程科学版).2006,38(3):38-42.
[81]屈亚玲,周建中,刘芳等.基于改进的Elman神经网络的径流中长期预报.水文.2006,26(1):45-50.
[82]Sohail A,Watanabe K,Takeuchi S.Runoff analysis for a small watershed of Tono area Japan by back propagation artificial neural network with seasonal data.Water Resources Management.2008,22(1):1-22.
[83]夏军.中长期径流预估的一种灰关联模式与预测方法.水科学进展.1993,4(3):190-197.
[84]冯平,杨鹏,李润苗.枯水期径流量的中长期预报模式.水利水电技术.1992,2):6-9.
[85]袁秀娟,夏军.径流中长期预报的灰色系统方法研究.武汉水力电力大学.1994,27(4):367-375.
[86]陈意平,李小牛.灰色系统理论在水利中的应用及前景人民珠江.1996,1):25-27.
[87]赵璀.灰色系统在瑞丽江长期水文预报中的应用.云南水力发电.2007,23(6):5-8.
[88]谢景新.非线性多步预测与优化方法及其在水文预报中的应用:(博士学位论文).大连:大连理工大学,2006.
[89]Breaford P W,Seyfried M S,Matison T H.Searching for chaotic dynamic in snowmelt runoff.Water Resources Research.1991,27(6):1005-1010.
[90]Jayawardena A W,Lai F.Analysis and prediction of chaos in rainfall and stream flow time series.Journal of Hydrology.1994,153(1-4):23-52.
[91]Sivakumar B,Berndtsson R,Persson M.Monthly runoff prediction using phase-space reconstruction.Hydrological Sciences Journal,2001,46(3):377-388.
[92]赵永龙,丁晶,邓育仁.相空间小波网络模型及其在水文中长期预测中的应用.水科学进展.1998,9(3):252-257.
[93]权先璋,蒋传文,张勇传.径流预报的混沌神经网络理论及应用.武汉城市建设学院学报.1999,16(3):33-37.
[94]丁涛,周惠成,黄建辉.混沌水文时间序列区间预测研究.水利学报.2004,34(12):15-21.
[95]陈南祥,黄强,曹连海等.径流序列的相空间重构神经网络预测模型.河海大学学报(自然科学版).2005,33(5):490-493.
[96]李红霞,许士国.月径流序列的混沌特征识别及Volterra自适应预测法的应用.水利学报.2007,38(6):760-766.
[97]Chui C K.小波分析导论.西安:西安交通大学出版社,1995.
[98]Cao L Y.Predicting chaotic time series with wavelet network Physica D.1995,85(1):225-238.
[99]尤卫红,杞明辉,段旭.小波变换在短期气候预测模型.高原气象.1999,18(1):39-46.
[100]李贤彬,丁晶,李后强等.子波分析及其在水文水资源中潜在应用.四川联合大学学报(工程科学版).1997,1(4):49-52.
[101]周惠成,彭勇.基于小波分解的月径流预测校正模型研究.系统仿真学报.2007,19(5):1104-1108.
[102]Koza J.Genetic Programming:On the Programming of Computers by Natural Selection.Cambridge,MA.:MIT Press,1992.
[103]Khu S T,Liong S Y,Babovic V.Genetic Programming and Its Application in Real-Time Runoff Forecasting.Journal of the American Water Resources Association.2001,37(2):439-451.
[104]Liong S Y,Gautam R R,Khu S T,et al.Genetic Programming:A new paradigm in rainfall runoff modeling.Journal of the American Water Resources Association.2002,38(3):705-718.
[105]王文川,程春田.基于遗传程序设计的中长期径流预报模型研究及应用.水电能源科学.2006,24(6):29-32.
[106]袁晓辉,张勇传,袁艳斌.基于遗传规划的径流预测新方法.水力发电.2006,32(8):11-13.
[107]Vapnik V.The Nature of Statistical Learning Theory.New York:Springer,1995.
[108]廖杰,王文圣,李跃清.支持向量机及其在径流预测中的应用.四川大学学报(工程科学版).2006,38(6):24-29.
[109]林剑艺,程春田.支持向量机在中长期径流预报中的应用.水利学报.2006,37(6):681-686.
[110]Yu P S,Chen S T,Chang I F.Support vector regression for real-time flood stage forecasting.Journal of Hydrology.2006,328(3-4):704-716.
[111]Khan M S,Coulibaly P.Application of support vector machine in lake water level prediction.Journal of Hydrologic Engineering.2006,11(3):199-205.
[112]Little J D C.The Use of Storage Water in a Hydroelectric System.Journal of the Operations Research Society of America.1955,3(2):187-197
[113]Yeh W G.Reservoir management and operation models:A state-of-the-art review,water Resources Research.1985,21(12):1797-1818.
[114]Simonovic S P.Reservoir systems analysis:closing gap between theory and practice.Journal of Water Resources Planning and Management.1992,118(3):262-280.
[115]Windsor J S.Optimization model for reservoir flood control.water Resources Research.1973,9(5):1103-1114.
[116]伍宏中.水电站群补偿径流调节的线性规划模型及其应用.水力发电学报.1998,60(1):10-22.
[117]Needham J,Watkins D,Lund J.Linear programming for flood control in the Iowa and Des Moines rivers.Journal of water Resources Planning and Management.2000,126(3):118-127.
[118]Peng C,Buras N.Dynamic operation of a surface water resources system.Water Resources Research.2000,36(9):2701-2709.
[119]倪二男,管晓宏,李仁厚.梯级水电系统组合优化调度算法研究.中国电机工程学报.1999,19(1):19-23.
[120]Barros M T L,Tsai F T C,Yang S L.Optimization of large-scale hydro power system operations.Journal of Water Resources Planning and Management.2003,129(3):178-188.
[121]Brannlund H,Sjelvgren D.Short term generations cheduling with security constraints.IEEE Trans on Power Systems.1988,3(1):310-316.
[122]朱继忠,徐国禹.用网流法求解水火电力系统有功负荷分配.系统工程理论与实践.1995,15(1):68-73.
[123]杨侃,张静怡,董增川.长江防洪系统网络分析分解协调优化调度研究.河海大学学报(自然科学版).2000,28(3):77-81.
[124]罗强,宋朝红,雷声隆.水库群系统非线性网络流规划法.武汉大学学报(工学版).2001,34(3):22-26.
[125]叶秉如.水资源系统优化规划和调度.北京:中国水利水电出版社,1999.
[126]Young G K.Finding Reservoir operating Rules.Hydraulic Am.Civ.Eng.1967,93(HY6):297-321.
[127]谭维炎,黄守信.应用随机动态规划进行水电站的优化调度.水利学报.1982,7(1-7.
[128]Labadie J W.Optimal operation of multireservoir systems:State-of-the-art review.Journal of water Resources Planning and Management.2004,130(2):93-111.
[129]倪建军,徐立中,李臣明等.水库调度决策研究综述.水利水电科技进展.2004,24(6):63-66.
[130]Fred N L.A Method to Eliminate solution Trapping In Applying Progressive optimality principle to short-term hydrothermal scheduling.IEEE Trans on Power Systems.1989,4(3):935-942.
[131]宗航,李承军,周建中等.POA算法在梯级水电站短期优化调度中的应用.水电能源科学.2003,21(1):45-47.
[132]马光文,王黎.遗传算法在水电站优化调度中的应用.水科学进展.1997,8(3):275-280.
[133]伍永刚,王定一.二倍体遗传算法求解梯级水电站日优化调度问题.水电能源科学.1999,17(3):31-34.
[134]王大刚,程春田,李敏.基于遗传算法的水电站优化调度研究.华北水利水电学院学报.2001,22(1):5-10.
[135]畅建霞,黄强,王义民.基于改进遗传算法的水电站水库优化调度.水力发电学报.2001,3):85-90.
[136]Wu Y G,Ho C Y,Wang D.A diploid genetic approach to short-term scheduling of hydro-thermal system.IEEE Trans.on Power Systems.2000,15(4):1268-1274.
[137]Oliveira R,Haunert D.Operating rules for multireservoir systems.Water Resources Research.1997,33(4):839-852.
[138]Chang F,Chen L,Chang L C.Optimizing the reservoir operating rule curves by genetic algorithms.Hydrological Processes.2005,19(11):2277-2289.
[139]钟登华,熊开智,成立芹.遗传算法的改进及其在水库优化调度中的应用研究.中国工程科学.2003,5(9):22-26.
[140]Ahmed J A,Sarma A K.Genetic Algorithm for optimal operating policy of a multipurpose reservoir.Water Resources Management.2005,19(2):145-161.
[141]王万良,周慕逊,管秋等.基于遗传算法的小水电站优化调度方法的研究与实践.水力发电学报.2005.24(3):6-11.
[142]Jothiprakash V,Shanthi G.Single reservoir operating policies using genetic algorithm.Water Resources Management.2006,20(6):917-929.
[143]胡铁松.水库调度智能决策支持系统理论与应用研究:(博士学位论文).武汉:武汉水利电力大学,1993.
[144]Park J H,Kim Y S,Lee K Y.Economic load dispatch for piecewise quadratic cost function using Hopfield neural network.IEEE Transactions on Power Systems.1993,8(3):1030-1038.
[145]Naresh R,Sharma J.Short term hydro scheduling using two-phase neural network.Electrical Power & Energy Systems.2002,24(6):583-590.
[146]Wong K P,Wong Y W.Short-term hydrothermal scheduling part 1:simulated annealing approach.IEE Proceedings-Generation,Transmission and Distribution.1994,141(5):497-501.
[147]Wong K P,Wong Y W.Short-term hydrothermal scheduling part 2:parallel simulated annealing approach.IEE Proceedings-Generation,Transmission and Distribution.1994,141(5):502-506.
[148]张双虎,黄强,孙廷容.基于并行组合模拟退火算法的水电站优化调度研究.水力发电学报.2004,23(4):16-19.
[149]袁晓辉,袁艳斌,权先璋等.基于混沌进化算法的梯级水电系统短期发电计划.电力系统自动化.2001,16(34-38.
[150]邱林,田景环,段春青等.混沌优化算法在水库优化调度中的应用.中国农村水利水电.2005,7(17-20.
[151]Dorigo M,Maniezzo V,Colorni A.The ant system:Optimization by a colony of cooperating agents.IEEE Transactions on Systems,Man,and Cybernetics.1996,1(26):29-41.
[152]Huang S J.Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches.IEEE Trans.on Power Systems.2001,16(3):296-301.
[153]徐刚,马光文,涂扬举.蚁群算法求解梯级水电厂日竞价优化调度问题.水利学报.2005,36(8):978-987.
[154]徐刚,马光文,梁武湖等.蚁群算法在水库优化调度中的应用.水科学进展.2005,16(3):397-400.
[155]王德智,董增川,丁胜祥.基于连续蚁群算法的供水水库优化调度.水电能源科学.2006,24(2):77-79.
[156]胡国强,贺仁睦.基于自适应蚁群算法的水电站水库优化调度.中国电力.2007,40(7):48-50.
[157]kenndey J,Eberhart R.Particle Swarm Optimization.IEEE Int'1 conf on Neural Networks,Perth,Australia,1995.
[158]武新宇,程春田,廖胜利.两阶段粒子群算法在水电站群优化调度中的应用.电网技术.2006,30(20):25-28.
[159]杨道辉,马光文,过夏明等.粒子群算法在水电站优化调度中的应用.水力发电学报.2006,25(5):5-7.
[160]张双虎,黄强,吴洪寿等。水电站水库优化调度的改进粒子群算法.水力发电学报.2007,26(1):1-5.
[161]陈功贵,杨俊杰,高仕红.基于混合PSO算法的梯级水库优化调度研究.水力发电.2007,33(10):86-88.
[162]Crawford N H,Linsley R K.Digital simulation in Hydrology:Stanford Watershed Model Ⅳ:Technical Report 39,Dept.of Civil Engineering,Stanmford Universitys,1966.
[163]Rockwood D M.Theory and practice of the SSARR model as related to analyzing and forecasting the response of hydrologic systems.In V.P,Singh,ed.Applied modeling in catchment hydrology.Littleton,Colo:Water Resources Publications,1982:87-106.
[164]Brazil L E,Hudlow M D.Calibration procedures used with the national weather service forecast system.In Y Y Haimes and J Kindler,ed.Water and Related Land Resources.Pergamon,New York,1981:457-466.
[165]Sugawara M.Tank model.In V.P.Singh,ed.Computer models of watershed hydrology.Littleton,Colo:Water Resources Publications,1995.
[166]赵人俊.流域水文模拟.北京:水利水电出版社,1984.
[167]Duan Q Y,Sorooshian S,Gupta V.Effective and Efficient Global Optimization for Conceptual Rainfall-runoff Models.Water Resources Research.1992,28(4):1015-1031.
[168]Cooper V A,Nguyen V T V,Nicell J A.Evaluation of global optimization methods for conceptual rainfall-runoff model calibration.Water Science and Technology.1997,36(5):53-60.
[169]Ndiritu J G,Daniell T M.An improved genetic algorithm for rainfall-runoff model calibration and function optimization.Mathematical and Computer Modelling.2001,33(6-7):695-706.
[170]陆桂华,郦建强,杨晓华.水文模型参数优选遗传算法的应用.水利学报.2004,02):50-56.
[171]Cheng C T,Ou C P,Chau K W.Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration.Journal of Hydrology.2002,268(1-4):72-86.
[172]Cheng C-T,Zhao M-Y,Chau K W,et al.Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure.Journal of Hydrology.2006,316(1-4):129-140.
[173]Zhao R-J.The Xinanjiang model applied in China.Journal of Hydrology.1992,135(1-4):371-381.
[174]Goldberg D E.Genetic algorithm in Search,Optimization and Machine Learning.Inc.,Reading,MA.:Addison-Wesley Publishing Co.,1989.
[175]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社,1999.
[176]李兵,蒋慰孙.混沌优化方法及其应用.控制理论与应用.1997,14(4):613-615.
[177]May R M.Simple mathematical models with very complicated dynamics.Nature.1976,261(459-467.
[178]康立山.非数值并行算法(第一册)-模拟退火算法.北京:科学出版社,1997.
[179]Laarhoven P J M V,Aarts E H L.Simulated Annealing:Theory and Applications.Dordrecht,The Netherlands:Kluwer,1987.
[180]Janikow C Z,Michalewicz Z.An experimental comparison of binary and floating point representation in genetic algorithms.Proceedings of the Fourth International Conference on Genetic Algorithms,San Francisco,1991:31-36.
[181]李耀华,徐乐江,胡国奋等.基于混沌遗传算法的板坯入库决策优化方法.系统仿真学报.2005,17(11):2620-2624.
[182]Boyle D P,Gupta H V,Sorooshian S.Toward improved calibration of hydrologic models:Combining the strengths of manual and automatic methods.Water Resources Research.2000,36(12):3663-3674.
[183]陈守煜.系统模糊决策理论与应用.大连:大连理工大学出版社,1994.
[184]长江水利委员会水文局,葛守西.现代洪水预报技术.北京:中国水利水电出版社,1999.
[185]Thiemann M,Trosset M,Gupta H.Bayesian recursive parameter estimation for hydrologic models.Water Resources Research.2001,37(10):2521-2535.
[186]Bates B C,Campbell E P.A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling.Water Resources Research.2001,37(4).
[187]Vrugt J A,Gupta H V,Dekker S C,et al.Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting Model.Journal of Hydrology.2006,325(1-4):288-307.
[188]Hapualrachchi H A P,Li Z-j,Wang S.Application of SCE-UA method for calibrating the Xin'anjiang watershed model.Journal of Lake Sciences.2001,12(4):304-314.
[189]刘力,周建中,杨俊杰等.基于改进粒子群优化算法的新安江模型参数优选.水力发电.2007,33(7):16-19.
[190]Sorooshian S,Duan Q Y,Gupta V K.Calibration of rainfall-runoff model:application of global optimization to the sacramento soil moisture accornting model.Water Resources Research.1993,29(4):1185-1194.
[191]Duan Q,Sorooshian S,Gupta V K.Optimal use of the SCE-UA global optimization method for calibrating watershed models.Journal of Hydrology.1994,158(3):265-284.
[192]Thyer M,Kuczera G,Bates B C.Probabilistic optimization for conceptual rainfall-runoff models:A comparison of the shuffled complex evolution and simulated annealing algorithms.Water Resources Research.1999,35(3):767-773.
[193]Franchini M,Galeati G,Berra S.Global optimization techniques for the calibration of conceptual rainfall-runoff models.Hydrological Sciences Journal.1998,43(3):443-458.
[194]Box G E P,Tiao G C.Bayesian infererce in Statistical Analyses.Addison-Wesley-Longman,Reading,Mass.,1973.
[195]Chen H L,Rao A R.Testing hydrologic time series for stationarity.Journal of Hydrologic Engineering.2002,7(2):129-136.
[196]Salas J D.Analysis and modeling of hydrologic time series.1993.
[197]Srikanthan R,McMahon T A.Stochastic generation of annual,monthly and daily climate data:A review.Hydrology and Earth System Sciences.2001,5(4):653-670.
[198]Committee A T.Artificial neural networks in hydrology-Ⅰ:Preliminary concepts.Journal of Hydrologic Engineering-ASCE.2000,5(2):115-123.
[199]Committee A T.Artificial neural networks in hydrology-Ⅱ:Hydrological applications.Journal of Hydrologic Engineering-ASCE.2000,5(2):124-137.
[200]Jang J-S R.ANFIS:Adaptive-Network-based Fuzzy Inference Systems.IEEE Transactions on Systems,Man,and Cybernetics.1993,23(3):665-685.
[201]Bazartseren B,Hildebrandt G,Holz K P.Short-term water level prediction using neural networks and neuro-fuzzy approach.Neurocomputing.2003,55(3-4):439-450.
[202]Nayak P C,Sudheer K P,Rangan D M,et al.A neuro-fuzzy computing technique for modeling hydrological time series.Journal of Hydrology.2004,291(1-2):52-66.
[203]geskin M E,Taylan D,Terzi O.Adaptive neural-based fuzzy inference system(ANFIS)approach for modelling hydrological time series.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.2006,51(4):588-598.
[204]马细霞,陈鑫,胡铁成.基于ANFIS模型的年径流预报方法.郑州大学学报(工学版).2007,28(3):111-124.
[205]林剑艺,程春田,陈艳.基于自适应模糊推理系统模型的径流中长期预报.水电能源科学.2005,23(6):25-27.
[206]Johari A,Habibagahi G,Ghahramani A.Prediction of soil-water characteristic curve using genetic programming.Journal of Geotechnical and Geoenvironmental Engineering.2006,132(5):661-665.
[207]Vapnik V.Statistical learning theory.New York:Wiley,1998.
[208]Chau K W,Cheng C T.Real-time prediction of water stage with artificial neural network approach.Lecture Notes in Artificial Intelligence.2002,2557(715.
[209]Haykin S.Neural networks:a comprehensive foundation.New Jersey:Upper Saddle River,1999.
[210]Jang J-S R,Sun C-T,Mizutani E.Neuro-Fuzzy and Soft Computing:A Computational Approach to Learning and Machine Intelligence.Upper Saddle River,NJ.:Prentice-Hall,1997.
[211]Koza J R,Bennett H B,DAndre,et al.Genetic Programming Ⅲ:Darwinian Invention and Problem Solving.San Francisco:CA:Morgan Kaufmann Publishers,1999.
[212]Sudheer K P,Gosain A K,Ramasastri K S.A data-driven algorithm for constructing artificial neural network rainfall-runoff models.Hydrological Processes.2002,16(6):1325-1330.
[213]Lin J Y,Cheng C T,Chau K W.Using support vector machines for long-term discharge prediction.Hydrological Sciences Journal-Journal Des Sciences Hydrologiques.2006,51(4):599-612.
[214]Santhi C,Arnold J G,Williams J R,et al.Validation of tbe swat model on a large river basin with point and nonpoint sources.Journal of the American Water Resources Association.2001,37(5):1169-1188.
[215]Yan Liew M W,Arnold J G,Garbrecht J D.Hydrologic simulation on agricultural watersheds:Choosing between two models.Transactions of the Asae.2003,46(6):1539-1551.
[216]Moriasi D N,Arnold J G,Van Liew M W,et al.Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Transactions of the ASABE.2007,50(3):885-900.
[217]Moiler M F.A scaled conjugate gradient algorithm for fast supervised learning.Neural Networks.1993,6(4):525-533.
[218]Liong S Y,Sivapragasam C.Flood stage forecasting with support vector machines.Journal of the American Water Resources Association.2002,38(1):173-186.
[219]Pai P-F,nong W-C.Support vector machines with simulated annealing algorithms in electricity load forecasting.Energy Conversion and Management.2005,46(17):2669-2688.
[220]Pai P-F,Hong W-C.Software reliability forecasting by support vector machines with simulated annealing algorithms.Journal of Systems and Software.2006,79(6):747-755.
[221]Yu X Y,Liong S Y,Babovic V.EC-SVM approach for real-time hydrologic forecasting.Journal of Hydroinformatics.2004,6(3):209-223.
[222]Duan Q Y,Gupta V K,SorooshianS.Shuffled complex evolution approach for effective and efficient minimization.Journal of Optimization Theory and Applications.1993,76(3):501-521.
[223]张亚乐,徐博文,一种改进的遗传算法在原油蒸馏过程优化中的应用.化工自动化及仪表.1997,24(3):12-17.
[224]唐巍,郭镇明,唐嘉亨等.复杂函数优化的混沌遗传算法.哈尔滨工程大学学报.2000,21(5):1-5.
[225]LU Q-z,Shen G-l,Yu R-q.A chaotic approach to maintain the population diversity of genetic algorithm in network training,Computational Biology and Chemistry.2003,27(3):363-371.
[226]Rosenbrock H H.An Automatic Method for Finding the Greatest or Least Value of a Function.Computer Journal.1960,3:175-184.
[227]Siarry P,Petrowski A,Bessaou M.A multipopulation genetic algorithm aimed at multimodal optimization.Advances in Engineering Software.2002,33(4):207-213.
[228]Wei G.Fast immunized evolutionary programming.Evolutionary Computation 2004,Congress,2004:666-670.