摘要：Background: Water is considered as the main source of life but water resources are limited and nonrenewable. Different factors have caused groundwater to decrease. Therefore, modeling and predicting groundwater level is of great importance. Methods: Monthly groundwater level data of about 20 years (October 1991 to February 2012) from the Hamadan-Bahar Plain, west of Iran were used based on peizometric height related to hydrologic years. The support vector machine (SVM), a new nonlinear regression technique, was used to predict groundwater level. The performance of the SVM model was assessed by using criteria of R 2 , root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE), correlation coefficient and efficiency coefficient (E) and was then compared with the classic time series model. Results: The SVM model had greater R 2 (=0.933), E (=0.950) and Correlation (=0.965). Moreover, SVM had lower RMSE (=0.120), MAPE (=0.140) and MAE (=0.124). There was no significant difference between the estimated values using two models and the observed value. Conclusions: The SVM outperforms classic time series model in predicting groundwater level. Therefore using the SVM model is reasonable for modeling and predicting fluctuations of groundwater level in Hamadan-Bahar Plain.
其他摘要：Background: Water is considered as the main source of life but water resources are limited and nonrenewable. Different factors have caused groundwater to decrease. Therefore, modeling and predicting groundwater level is of great importance. Methods: Monthly groundwater level data of about 20 years (October 1991 to February 2012) from the Hamadan-Bahar Plain, west of Iran were used based on peizometric height related to hydrologic years. The support vector machine (SVM), a new nonlinear regression technique, was used to predict groundwater level. The performance of the SVM model was assessed by using criteria of R 2 , root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE), correlation coefficient and efficiency coefficient (E) and was then compared with the classic time series model. Results: The SVM model had greater R 2 (=0.933), E (=0.950) and Correlation (=0.965). Moreover, SVM had lower RMSE (=0.120), MAPE (=0.140) and MAE (=0.124). There was no significant difference between the estimated values using two models and the observed value. Conclusions: The SVM outperforms classic time series model in predicting groundwater level. Therefore using the SVM model is reasonable for modeling and predicting fluctuations of groundwater level in Hamadan-Bahar Plain.
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