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

ISSN号：1000-7709
期刊名称：《水电能源科学》
时间：0
分类：P338[天文地球—水文科学;水利工程—水文学及水资源;天文地球—地球物理学] U675.6[交通运输工程—船舶及航道工程;交通运输工程—船舶与海洋工程]
作者机构：[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
相关基金：This work was supported by the National Basic Research Program of China （the 973 Program, Grant No 2010CB951101） and the Program for Changjiang Scholars and Innovative Research Teams in Universities the Ministry of Education, China （Grant No. IRT0717）.

作者： Di LIU[1], Zhong-bo YU[1,2], Hai-shen LV[1]

关键词：卡尔曼滤波模型, 土壤水分, 支持向量机, 墒情预报, 数据同化, 集合, 多层次, SVM方法, data assimilation, support vector machines, ensemble Kalman filter, soil moisture

中文摘要：

混合数据吸收(DA ) 是在最近的水文学和水资源研究看见更多的使用的一个方法。在这研究，一个 DA 方法结合了支持向量机器(SVM ) ，整体 Kalman 过滤器(EnKF ) 技术在不同土壤层被用于土壤潮湿的预言：0-5 厘米， 30 厘米， 50 厘米， 100 厘米， 200 厘米，和 300 厘米。SVM 方法论首先被用来从 Meilin 学习区域训练土壤潮湿和气象学的参数的扎根的大小，在华东，到统计预言建模的构造土壤潮湿。随后的观察和他们的统计被用于预言，与二条途径：SVM 预言者和联合 SVM 做的 SVM-EnKF 模型用 DA 方法与 EnKF 技术当模特儿。确认结果证明建议 SVM-EnKF 模型能在不同的层改进土壤潮湿的预言结果，从表面到根地区。

英文摘要：

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.

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