Reservoir Inflow Prediction Using Hybrid Data Assimilation and Support Vector Machine Model

سال انتشار: 1393
محل انتشار: هشتمین کنگره ملی مهندسی عمران
کد COI اختصاصی: NCCE08_0204
زبان مقاله: انگلیسی
تعداد مشاهده: 1399

نویسندگان

Ph.D. student, Department of Civil Engineering, Isfahan University of Technology

Assistant Professor, Department of Civil Engineering, Isfahan University of Technology

چکیده

Accurate and reliable reservoir inflow forecast is crucial for real-time reservoir operation and management. Developing a simulation model to forecast future states of a system generally follows with errors in prediction. Frequently, data-based models such as Support Vector Machines (SVM) are used as simulation and forecasting techniques in hydrologicalmodeling. This paper introduces a hybrid model which couples the machine learning technique of Support VectorRegression for prediction (SVR) and Ensemble Kalman Filter (EnKF) as data assimilation procedure in order to improvethe performance and accuracy of inflow to reservoir. Inflow quantities to Zayandehroud reservoir is considered as state vector in assimilation process to develop better estimation for inflow. Evaluation criteria such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are implemented to evaluate the performance of SVR-EnKF model. As a result, the adjusted values of SVR-EnKF compared to SVR model indicate improved performance of proposed model

کلیدواژه ها

Reservoir Inflow Prediction, Support Vector Regression, Ensemble Kalman Filter, Data Assimilation

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