期刊
WATER RESOURCES MANAGEMENT
卷 29, 期 5, 页码 1557-1574出版社
SPRINGER
DOI: 10.1007/s11269-014-0894-6
关键词
Lake level fluctuations; Neuro-fuzzy; Grid partition; Subtractive clustering; Genetic programming
Modeling lake level fluctuation is very essential for planning and design of hydraulic structures along the lake coasts. In this study, namely two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast 1-, 2- and 3-month ahead lake-level fluctuations of Manyas and Tuz, Turkey. Comparison of the models indicated that the optimal ANFIS-GP models performed better than the optimal ANFIS-SC and GEP models in forecasting 1- and 3-month ahead lake levels while the ANFIS-SC model showed better accuracy than the other models in 2-month ahead forecasting. The ANFIS-GP model comprising lake level values of current and one previous months successfully forecasted 1-month ahead lake level with root mean square error (RMSE) of 0.251 and coefficient of determination (R-2) of 0.872. For the Tuz Lake, the optimal ANFIS-SC models were found to be better than the optimal ANFIS-GP and GEP models for forecasting 1- and 2-month ahead lake levels while the GEP model performed better than the other models in and 3-month ahead lake level forecasting. The ANFIS-SC model comprising lake level values of current and three previous months successfully forecasted 1-month ahead lake level with RMSE of 0.120 and R-2 of 0.724. Based on the comparisons, it was found that the GEP, ANFIS-GP and ANFIS-SC models could be successfully employed in forecasting lake level fluctuations.
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