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Soil Moisture Data Assimilation Using Support Vector Machines and Ensemble Kalman Filter1

Authors

  • M. Kashif Gill,

    1. Respectively, Graduate Research Assistant, Department of Civil and Environmental Engineering and the Utah Water Research Laboratory, Utah State University, Logan, Utah 84322-8200 [Kashif Gill currently Postdoctoral Research Associate at Pacific Northwest National Laboratory, Richland, Washington 99352]
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  • Mariush W. Kemblowski,

    1. Professor (Kemblowski and McKee), Department of Civil and Environmental Engineering and the Utah Water Research Laboratory, Utah State University, Logan, Utah 84322-8200 (E-Mail/Kashif Gill: kashif@cc.usu.edu).
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  • Mac McKee

    1. Professor (Kemblowski and McKee), Department of Civil and Environmental Engineering and the Utah Water Research Laboratory, Utah State University, Logan, Utah 84322-8200 (E-Mail/Kashif Gill: kashif@cc.usu.edu).
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  • 1

    Paper No. J05081 of the Journal of the American Water Resources Association (JAWRA). Discussions are open until February 1, 2008.

Abstract

Abstract:  A hybrid data assimilation (DA) methodology that combines two state-of-the-art techniques, support vector machines (SVMs) and ensemble Kalman filter (EnKF), is applied for soil moisture DA in this work. The SVM methodology provides a statistically sound and robust approach to solving the inverse problem, and thus to building statistical models. EnKF is an extension of the Kalman Filter (KF), a well-known tool in prediction updating. In the present research, ground measurements were used to build a SVM-type soil moisture predictor. Subsequent observations and their statistics were assimilated to update predictions from the SVM model by coupling it with EnKF. In this way, both model predictions and ground data, as well as their statistics, are fused thus minimizing the prediction error and making the predictions and observations statistically consistent. The results are shown for two approaches; one in which update is done at every time step and the other which assumes that data is only available at alternate time steps (in window of 10 time steps) and hence update is performed at those occasions. The SVM-EnKF coupling is shown to improve soil moisture forecasts in an example using data from the Soil Climate Analysis Network site at Ames, Iowa.

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