水文时间序列预测模型研究进展
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摘要
水文时间序列具有确定成分和随机成分,运用有效的数学方法提取序列样本中的各个成分,通过数学建模的思想,模拟和预测时间序列是近年来水文预报的重要发展方向。与以往过程驱动模型不同,近年来水文预报的模型主要是数据驱动模型,这类模型分为传统方法和新方法两大类。前者主要有成因分析和水文统计方法等,后者包括近年来新兴的模糊分析、人工神经网络、灰色系统分析、支持向量机等。对目前运用于时间序列预测的几个代表方法的模型特点、适用范围和不足进行了简要评述,展望了水文时间序列预测模型未来发展,期望能为水文预报工作提供帮助。
The hydrological time series is featured with certain components and random components.It is an important development direction of hydrological forecast to extract the components in the sequence samples by effective mathematical methods, and simulate & forecast the time series through the idea of mathematical modeling in recent years.Different from the previous process-driven model, the models of hydrological forecast in recent years are mainly data-driven models, on which the methods based are divided intotraditional methods and new methods.The former mainly includes cause analysis and hydrological statistics, while the latter mainly includes fuzzy analysis, artificial neural network, gray system analysis and support vector machine.This paper briefly reviews the model characteristics, scope of application and deficiencies of several representative methods currently applied to time series prediction, and forecasts the development of hydrological time series prediction models in the future. It is expected to provide assistance for hydrological forecast.
The hydrological time series is featured with certain components and random components.It is an important development direction of hydrological forecast to extract the components in the sequence samples by effective mathematical methods, and simulate & forecast the time series through the idea of mathematical modeling in recent years.Different from the previous process-driven model, the models of hydrological forecast in recent years are mainly data-driven models, on which the methods based are divided intotraditional methods and new methods.The former mainly includes cause analysis and hydrological statistics, while the latter mainly includes fuzzy analysis, artificial neural network, gray system analysis and support vector machine.This paper briefly reviews the model characteristics, scope of application and deficiencies of several representative methods currently applied to time series prediction, and forecasts the development of hydrological time series prediction models in the future. It is expected to provide assistance for hydrological forecast.
引文
[1] 汤成友,官学文,张世明.现代中长期水文预报方法及其应用[M].北京:中国水利水电出版社,2008.
[2] 桑燕芳,王中根,刘昌明.水文时间序列分析方法研究进展[J].地理科学进展,2013,32(1):20-30.
[3] 王文圣,丁晶,金菊良.随机水文学[M].2版.北京:中国水利水电出版社,2008.
[4] 杜懿.水文时间序列的特性分析及预测研究[D].南宁:广西大学,2018.
[5] 孙鹏,张强,涂新军,等.基于马尔科夫链模型的鄱阳湖流域水文气象干旱研究[J].湖泊科学,2015,24(6):1177-1186.
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[7] 任化准.基于主成分分析的GA-SVM模型在中长期径流预报中的应用[J].水利科技与经济,2017,23(2):48-51.
[8] 刘德地,陈晓宏.基于偏最小二乘回归与支持向量机耦合的咸潮预报模型[J].中山大学学报(自然科学版),2007,46(4):89-92.
[9] 徐莹,王嘉阳,苏华英.基于遗传算法的支持向量机在径流中长期预报中的应用[J].水利与建筑工程学报,2014,12(5):42-45.
[10] 张俊,程春田,申建建,等.基于蚁群算法的支持向量机中长期水文预报模型[J].水力发电学报,2010,29(6):34-40.
[11] 张岩,杨明祥,雷晓辉,等.基于PCA-PSO-SVR的丹江口水库年径流预报研究[J].南水北调与水利科技,2018,16(5):35-40.
[12] 邱超.模糊聚类分析在水文预报中的研究及应用[D].杭州:浙江大学,2007.
[13] 周念来,吴泽宁.水文要素模糊因果聚类预报及其应用[J].郑州工业大学学报,2000,21(1):58-61.
[14] 丁亚明,赵艳平,张志红,等.基于主成分分析和模糊聚类的水文分区[J].合肥工业大学学报(自然科学版),2009,32(6):796-801.
[15] 郭瑜.半监督迭代模糊聚类模型及其在中长期水文预报中的应用[J].东南大学学报(自然科学版),2013,43(1):59-62.
[16] 宋亚娅,张振,黄振平,等.基于模糊识别理论的无资料地区设计径流典型年选择[J].河海大学学报(自然科学版),2014,42(1):19-23.
[17] 宋亚娅,张振,黄振平.基于模糊加权识别模型的相似流域优选研究[J].人民长江,2014,45(21):54-57.
[18] 李磊,秦福刚,沈慧珍.模糊识别方法在潘北地下水水源判别中的应用[J].淮南职业技术学院学报,2005,5(1):18-20.
[19] 吴恒卿,黄强,习树峰.基于熵权的可变模糊聚类与识别的水库洪水分类实时预报[J].水力发电学报,2015,34(2):57-63.
[20] 任海青.基于灰色系统的年降雨量预测模型探析[J].水资源开发与管理,2017,18(3):61-63.
[21] 张辉.灰色理论在中长期水文预报中的应用研究[J].地下水,2014,36(6):164-165.
[22] 邵年华.水文时间序列几种预测方法比较研究[D].西安:西安理工大学,2010.
[23] FRENCH M N,KRAJEWSKI W F,CUYKENDAL R R.Rainfall forecasting in space and time using a neural network[J].Journal of Hydrology,1992,137(1—4):1-31.
[24] HSU K L,GUPTA H V,SOROOSHIAN S .Artificial networks modeling of the rainfall-runoff process[J].Water Resource,1995,31(10):2530-2571.
[25] 吴超羽,张文.水文预报的人工神经网络方法[J].中山大学学报(社会科学版),1994,33(1):79-90.
[26] 苑希民,李鸿雁,刘树坤.神经网络和遗传算法在水科学领域的应用[M].北京:中国水利水电出版社,2002.
[27] 李敬库.人工神经网络在水资源规划领域中的应用[J].水资源开发与管理,2017,5(3):13-16.
[28] 王鹏,李立,谢鹏.中长期水文预报神经网络模型结构的确定及应用研究[J].江苏科技信息,2017,16(3):27-31.
[29]刘忠民.小波分析和人工神经网络及水文频率分析法相结合的中长期水文预报模型研究[J].水利技术监督,2018,25(3):79-81.
[30]朱跃龙,李士进,范青松,等.基于小波神经网络的水文时间序列预测[J].山东大学学报(工学版),2011,41(4):119-124.
[31]FOUFOULA-GEORGIOU E,KUMAR P.Wavelets in geophysics[R].San Diego Academic,1994.
[32]LABAT D.Recent advances in wavelet analyses[J].A review of concepts,2005,14(3):275-288.
[33]LABAT D.Oscillations in land surface hydrological cycle [J].Earth and Planetary Science Letters,2006,42(2):153-154.
[34]LABAT D.Wavelet analysis of the annual discharge records of the world′s largest rivers[J].Advances in wavelet analysis,2008(3):109-117.
[35]SCHAEFLI B,MARAUN D,HOLSCHNEIDER M.What drives high flow events in the Swiss Alps?Recent developments in wavelet spectral analysis and their application to hydrology[J].Advances in Water Resources,2007,30:2511-2525.
[36]COULIBALY P,BURN D H.Wavelet analysis of variability in annual Canadian streamflows[J].Water Resource Research,doi:10.1029/2003WR002667,2004.
[37]GAUCHEREL C.Use of wavelet transform for temporal characterization of remote watersheds[J].Journal of Hydrology,2002,269(3/4):101-121.
[38]NAKKEN M.Wavelet Analysis of Rainfall-runoff Variability Isolating Climatic from Anthropogenic Patterns[J].Environmental Modelling & Software,1999,14(4):283-295.
[39]王文圣,丁晶,向红莲.小波分析在水文学中的应用研究及展望[J].水科学进展,2002,13(4):515-520.
[40]王文圣,丁晶,李跃清.水文小波分析[M].北京:化学工业出版社,2005.
[2] 桑燕芳,王中根,刘昌明.水文时间序列分析方法研究进展[J].地理科学进展,2013,32(1):20-30.
[3] 王文圣,丁晶,金菊良.随机水文学[M].2版.北京:中国水利水电出版社,2008.
[4] 杜懿.水文时间序列的特性分析及预测研究[D].南宁:广西大学,2018.
[5] 孙鹏,张强,涂新军,等.基于马尔科夫链模型的鄱阳湖流域水文气象干旱研究[J].湖泊科学,2015,24(6):1177-1186.
[6] VAPNIK V.The Nature of Statistical Learning Theory [M].New York:springer-verlag,1995.
[7] 任化准.基于主成分分析的GA-SVM模型在中长期径流预报中的应用[J].水利科技与经济,2017,23(2):48-51.
[8] 刘德地,陈晓宏.基于偏最小二乘回归与支持向量机耦合的咸潮预报模型[J].中山大学学报(自然科学版),2007,46(4):89-92.
[9] 徐莹,王嘉阳,苏华英.基于遗传算法的支持向量机在径流中长期预报中的应用[J].水利与建筑工程学报,2014,12(5):42-45.
[10] 张俊,程春田,申建建,等.基于蚁群算法的支持向量机中长期水文预报模型[J].水力发电学报,2010,29(6):34-40.
[11] 张岩,杨明祥,雷晓辉,等.基于PCA-PSO-SVR的丹江口水库年径流预报研究[J].南水北调与水利科技,2018,16(5):35-40.
[12] 邱超.模糊聚类分析在水文预报中的研究及应用[D].杭州:浙江大学,2007.
[13] 周念来,吴泽宁.水文要素模糊因果聚类预报及其应用[J].郑州工业大学学报,2000,21(1):58-61.
[14] 丁亚明,赵艳平,张志红,等.基于主成分分析和模糊聚类的水文分区[J].合肥工业大学学报(自然科学版),2009,32(6):796-801.
[15] 郭瑜.半监督迭代模糊聚类模型及其在中长期水文预报中的应用[J].东南大学学报(自然科学版),2013,43(1):59-62.
[16] 宋亚娅,张振,黄振平,等.基于模糊识别理论的无资料地区设计径流典型年选择[J].河海大学学报(自然科学版),2014,42(1):19-23.
[17] 宋亚娅,张振,黄振平.基于模糊加权识别模型的相似流域优选研究[J].人民长江,2014,45(21):54-57.
[18] 李磊,秦福刚,沈慧珍.模糊识别方法在潘北地下水水源判别中的应用[J].淮南职业技术学院学报,2005,5(1):18-20.
[19] 吴恒卿,黄强,习树峰.基于熵权的可变模糊聚类与识别的水库洪水分类实时预报[J].水力发电学报,2015,34(2):57-63.
[20] 任海青.基于灰色系统的年降雨量预测模型探析[J].水资源开发与管理,2017,18(3):61-63.
[21] 张辉.灰色理论在中长期水文预报中的应用研究[J].地下水,2014,36(6):164-165.
[22] 邵年华.水文时间序列几种预测方法比较研究[D].西安:西安理工大学,2010.
[23] FRENCH M N,KRAJEWSKI W F,CUYKENDAL R R.Rainfall forecasting in space and time using a neural network[J].Journal of Hydrology,1992,137(1—4):1-31.
[24] HSU K L,GUPTA H V,SOROOSHIAN S .Artificial networks modeling of the rainfall-runoff process[J].Water Resource,1995,31(10):2530-2571.
[25] 吴超羽,张文.水文预报的人工神经网络方法[J].中山大学学报(社会科学版),1994,33(1):79-90.
[26] 苑希民,李鸿雁,刘树坤.神经网络和遗传算法在水科学领域的应用[M].北京:中国水利水电出版社,2002.
[27] 李敬库.人工神经网络在水资源规划领域中的应用[J].水资源开发与管理,2017,5(3):13-16.
[28] 王鹏,李立,谢鹏.中长期水文预报神经网络模型结构的确定及应用研究[J].江苏科技信息,2017,16(3):27-31.
[29]刘忠民.小波分析和人工神经网络及水文频率分析法相结合的中长期水文预报模型研究[J].水利技术监督,2018,25(3):79-81.
[30]朱跃龙,李士进,范青松,等.基于小波神经网络的水文时间序列预测[J].山东大学学报(工学版),2011,41(4):119-124.
[31]FOUFOULA-GEORGIOU E,KUMAR P.Wavelets in geophysics[R].San Diego Academic,1994.
[32]LABAT D.Recent advances in wavelet analyses[J].A review of concepts,2005,14(3):275-288.
[33]LABAT D.Oscillations in land surface hydrological cycle [J].Earth and Planetary Science Letters,2006,42(2):153-154.
[34]LABAT D.Wavelet analysis of the annual discharge records of the world′s largest rivers[J].Advances in wavelet analysis,2008(3):109-117.
[35]SCHAEFLI B,MARAUN D,HOLSCHNEIDER M.What drives high flow events in the Swiss Alps?Recent developments in wavelet spectral analysis and their application to hydrology[J].Advances in Water Resources,2007,30:2511-2525.
[36]COULIBALY P,BURN D H.Wavelet analysis of variability in annual Canadian streamflows[J].Water Resource Research,doi:10.1029/2003WR002667,2004.
[37]GAUCHEREL C.Use of wavelet transform for temporal characterization of remote watersheds[J].Journal of Hydrology,2002,269(3/4):101-121.
[38]NAKKEN M.Wavelet Analysis of Rainfall-runoff Variability Isolating Climatic from Anthropogenic Patterns[J].Environmental Modelling & Software,1999,14(4):283-295.
[39]王文圣,丁晶,向红莲.小波分析在水文学中的应用研究及展望[J].水科学进展,2002,13(4):515-520.
[40]王文圣,丁晶,李跃清.水文小波分析[M].北京:化学工业出版社,2005.
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