Forecasting Monthly Water Level Fluctuations of Lake Urmia Using Supervised Committee Machine Artificial Intelligence Model

Authors

1 Ph.D student of Hydrogeology, Dept. of Earth Sciences, Faculty of Natural Sciences, Univ. of Tabriz, Iran

2 Prof., Dept. of Earth Sciences, Faculty of Natural Sciences. Univ. of Tabriz, Iran

3 Assist. Prof., School of Geology, University College of Science, University of Tehran, Tehran, Iran

Abstract

In recent years, declining the water level of Lake Urmia has caused water and environmental crisis in the area. Therefore, it is urgent to carry out an accurate and reliable management and planning which requires modeling the lake's water level for the future. In this research, the artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) models were used to forecast the Lake Urmia water level fluctuations for one, two and three months ahead forecast horizons and finally, a supervised committee machine artificial intelligence (SCMAI) model was used to obtain a better performance than the used individual models. To develop the models, the current month [h (t)] and eleven months water level lags [h (t-1),…, h (t-11)] were introduced as input variables to forecast one, two and three steps ahead water levels. The datasets were divided into two subsets of training/validation (90%) and testing (10%). The performances of the models were evaluated based on the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE). The results showed that the SVM models had better performance than the ANN and ANFIS models. The SCMAI model was applied to combine the used models’ outputs and illustrated that the SCMAI models are able to improve the performance of the individual artificial intelligence models. The results of the performance criteria for SCMAI model indicated that the one month step ahead water level modeling with R2, RMSE and MAE equal to 0.9896, 0.0547 m and 0.0421 m, respectively outperformed in comparison with SVM model which this performance is reliable for the two and three months step ahead lake's water level.

Keywords

References

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Water and Soil Science
Volume 29, Issue 1
April 2019
Pages 151-163

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