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Water Resources Management

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01-10-2014

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

Authors: Meral Buyukyildiz, Gulay Tezel, Volkan Yilmaz

Published in: Water Resources Management | Issue 13/2014

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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 (R²). According to the results of models, ε-SVR model was obtained as the most successful model to estimate monthly water level of Lake Beysehir.

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Title: Estimation of the Change in Lake Water Level by Artificial Intelligence Methods
Authors: Meral Buyukyildiz
Gulay Tezel
Volkan Yilmaz
Publication date: 01-10-2014
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 13/2014
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-014-0773-1

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