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

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  • Meral Buyukyildiz
  • Gulay Tezel
  • Volkan Yilmaz

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

Suggested Citation

  • Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:13:p:4747-4763
    DOI: 10.1007/s11269-014-0773-1
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    References listed on IDEAS

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