Estimation of the Change in Lake Water Level by Artificial Intelligence Methods

Meral Buyukyildiz (), Gulay Tezel () and Volkan Yilmaz ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 13, 4747-4763

Abstract: In this study, five different artificial intelligence methods, including Artificial Neural Networks based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks (MLP), Radial Basis Neural Networks (RBNN) and Adaptive Network Based Fuzzy Inference System (ANFIS), were used to estimate monthly water level change in Lake Beysehir. By using different input combinations consisting of monthly Inflow - Lost flow (I), Precipitation (P), Evaporation (E) and Outflow (O), efforts were made to estimate the change in water level (L). Performance of models established was evaluated using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R 2 ). According to the results of models, ε-SVR model was obtained as the most successful model to estimate monthly water level of Lake Beysehir. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Adaptive network-based fuzzy inference system; Artificial neural networks; Lake Beysehir; Particle swarm optimization; Support vector regression; Water level (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11269-014-0773-1

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