Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction  

作　　者：Di LIU Zhong-bo YU Hai-shen LV 

机构地区：[1]State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, P. R. China [2]Department of Geoscience, University of Nevada Las Vegas, Las Vegas 89154, USA

出　　处：《Water Science and Engineering》2010年第4期361-377,共17页水科学与水工程（英文版）

基　　金：supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951101);the Program for Changjiang Scholars and Innovative Research Teams in Universities,the Ministry of Education,China (Grant No. IRT0717)

摘　　要：Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter （EnKF） technology was used for the prediction of soil moisture in different soil layers： 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches： the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter （EnKF） technology was used for the prediction of soil moisture in different soil layers： 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches： the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.

关 键 词：data assimilation support vector machines ensemble Kalman filter soil moisture 

分 类 号：P338[天文地球—水文科学] U675.6[水利工程—水文学及水资源]