基于CAR-SVM模型的季节性冻融区地下水埋深预测
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摘要
准确预测地下水埋深是灌区水资源管理的重要依据.考虑到地下水埋深在时间序列上呈现滞后性和非线性,耦合了多变量时间序列CAR与支持向量机SVM,构建了CAR-SVM地下水埋深预测模型.为了提高模型在冻融期的模拟效果,构建了季节性冻融灌区地下水埋深拟合模型——CAR-SVM(T-TF)模型.模拟结果显示,只考虑冻融期气温的CAR-SVM(T-TF)模型优于考虑全年气温的CAR-SVM(T)模型及不考虑气温的CAR-SVM模型.CAR-SVM(TTF)模型在全灌区地下水埋深的模拟结果:在验证期模型决定系数R2为0.954,冻融期R2为0.973;RMSE均小于0.090 m,模型精度较高.将全灌区得到的3阶CAR-SVM(T-TF)模型结构用于灌区内5个灌域地下水埋深模拟,模型在各灌域均有较好的适用性.
Accurately predicting water table depth is an important basis for water resources management in irrigation areas. Based on hysteresis and nonlinearity of groundwater in time series,a CAR-SVM water table depth prediction model was developed by integrating multivariate time series controlled auto-regressive( CAR) and support vector machine( SVM). To improve the performance of the model in freezing-thawing period,a water table depth fitting model,i. e. CAR-SVM( T-TF) model,was established for seasonal freezing-thawing irrigation district. Simulation results indicate that the performance of the CAR-SVM( T-TF) model with ambient temperature effect in the freezing-thawing period is better than that either by the CAR-SVM( T) model with ambient temperature effect of the whole year or by the CAR-SVM without any ambient temperature effect. The CAR-SVM( T-TF) model was applied to predict the water table depth in Hetao Irrigation District. The results demonstrate that the coefficient of multiple determination,R2,is 0. 954 and 0. 973 in validation period and freezing-thawing period,respectively,and all the RMSE in different periods are less than 0. 090 m,suggesting a relatively high accuracy. The 3-order CAR-SVM( T-TF) model structure obtained from Hetao Irrigation District as a whole was used to simulate the water table depths in five irrigation areas in the district. The model has a good applicability in each area.
Accurately predicting water table depth is an important basis for water resources management in irrigation areas. Based on hysteresis and nonlinearity of groundwater in time series,a CAR-SVM water table depth prediction model was developed by integrating multivariate time series controlled auto-regressive( CAR) and support vector machine( SVM). To improve the performance of the model in freezing-thawing period,a water table depth fitting model,i. e. CAR-SVM( T-TF) model,was established for seasonal freezing-thawing irrigation district. Simulation results indicate that the performance of the CAR-SVM( T-TF) model with ambient temperature effect in the freezing-thawing period is better than that either by the CAR-SVM( T) model with ambient temperature effect of the whole year or by the CAR-SVM without any ambient temperature effect. The CAR-SVM( T-TF) model was applied to predict the water table depth in Hetao Irrigation District. The results demonstrate that the coefficient of multiple determination,R2,is 0. 954 and 0. 973 in validation period and freezing-thawing period,respectively,and all the RMSE in different periods are less than 0. 090 m,suggesting a relatively high accuracy. The 3-order CAR-SVM( T-TF) model structure obtained from Hetao Irrigation District as a whole was used to simulate the water table depths in five irrigation areas in the district. The model has a good applicability in each area.
引文
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[2]黄一帆,刘俊民,姜鹏,等.基于Modflow的泾惠渠地下水动态及预测研究[J].水土保持研究,2014,21(2):273-278.HUANG Yifan,LIU Junmin,JIANG Peng,et al.Research for groundwater dynamic and forecasting of Jinghui Irrigation Zone based on Modflow[J].Research of soil and water conservation,2014,21(2):273-278.(in Chinese)
[3]李彩梅,杨永刚,秦作栋,等.基于FEFLOW和GIS技术的矿区地下水动态模拟及预测[J].干旱区地理,2015,38(2):359-367.LI Caimei,YANG Yonggang,QIN Zuodong,et al.Simulation and prediction on variations of groundwater in mining area based on FEFLOW and GIS[J].Arid land geography,2015,38(2):359-367.(in Chinese)
[4]YAO Junqiang,LIU Zhihui,YANG Qing,et al.Responses of runoff to climate change and human activities in the Ebinur Lake catchment,western China[J].Water resources,2014,41(6):738-747.
[5]王蒙蒙,戴凌全,戴会超,等.基于支持向量回归的洞庭湖水位快速预测[J].排灌机械工程学报,2017,35(11):954-961.WANG Mengmeng,DAI Lingquan,DAI Huichao,et al.Support vector regression based model for predicting water level of Dongting Lake[J].Journal of drainage and irrigation machinery engineering,2017,35(11):954-961.(in Chinese)
[6]SURYANARAYANA C,SUDHEER C,MAHAMMOOD V,et al.An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam,India[J].Neurocomputing,2014,145:324-335.
[7]EBRAHIMI H,RAJAEE T.Simulation of groundwater level variations using wavelet combined with neural network,linear regression and support vector machine[J].Global and planetary change,2017,148:181-191.
[8]张展羽,梁振华,冯宝平,等.基于主成分-时间序列模型的地下水位预测[J].水科学进展,2017,28(3):415-420.ZHANG Zhanyu,LIANG Zhenhua,FENG Baoping,et al.Groundwater level forecast based on principal component analysis and multivariate time series model[J].Advances in water science,2017,28(3):415-420.(in Chinese)
[9]SUJAY RAGHAVENDRA N,DEKA P C.Support vector machine applications in the field of hydrology:a review[J].Applied soft computing,2014,19:372-386.
[10]杨文元,郝培静,朱焱,等.季节性冻融区井渠结合灌域地下水动态预报[J].农业工程学报,2017,33(4):137-145.YANG Wenyuan,HAO Peijing,ZHU Yan,et al.Groundwater dynamics forecast under conjunctive use of groundwater and surface water in seasonal freezing and thawing area[J].Transactions of the CSAE,2017,33(4):137-145.(in Chinese)
[11]王枭,杨培岭,苏艳平.温室番茄对微小流量滴灌埋深与灌水量的响应[J].排灌机械工程学报,2018,36(10):937-942.WANG Xiao,YANG Peiling,SU Yanping.Response of greenhouse tomatoes to buried depth and low irrigation limit of small flow drip irrigation[J].Journal of drainage and irrigation machinery engineering,2018,36(10):937-942.(in Chinese)
[12]李玉营,马东方,王书平,等.孕穗期地下水埋深对小麦产量及品质的影响[J].江苏农业科学,2016,44(12):107-110.LI Yuying,MA Dongfang,WANG Shuping,et al.Effects of groundwater depth at booting stage on yield and quality of wheat[J].Jiangsu agricultural sciences,2016,44(12):107-110.(in Chinese)
[13]刘艳伟,王淑莹,屠星磊,等.元谋干热河谷区近60年干湿状况和气温变化特征分析[J].排灌机械工程学报,2018,36(2):172-178.LIU Yanwei,WANG Shuying,TU Xinglei,et al.Characteristic analysis of dry-wet condition and temperature trend in Yuanmou dry-hot valley(DHV)in resent60 years[J].Journal of drainage and irrigation machinery engineering,2018,36(2):172-178.(in Chinese)