Evaluation of Stochastic Models in Predicting the Underground Water Level of Hamadan-Bahar Plain
Subject Areas : Optimal management of water and soil resourcesHamed Nozari 1 , Nadia Sedghnejad 2 , Sajjad Pouyanfar 3
1 - Associate Professor, Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali-Sina University, Hamedan.
2 - PhD student, Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali-Sina University, Hamedan.
3 - Master's degree, Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali-Sina University, Hamedan.
Keywords: Artificial Intelligence, Support Vector Machine Model (SVM), Simulated annealing (SA), ARIMA, SARIMA,
Abstract :
Background and Aim: Groundwater sources are considered to be one of the most important available sources of fresh water in the world. Today, due to the changes in weather, climate change, population increase and excessive withdrawals of underground water, these resources have faced a significant decrease. Considering that Iran is located in a dry and semi-arid region, the underground water level has undergone many changes in many areas. The level of underground water in Hamadan-Bahar plain area has also faced a significant decrease. Therefore, the prediction of underground water levels in Hamadan-Bahar plain and the management of effective factors in its reduction are the main goals of this research.
Method: In the first step, in this research, it was tried to predict the underground water level with the help of support vector machine integrated model with Simulated annealing algorithm (SVM-SA) using the rainfall values of 4 synoptic stations of Aghkahriz, Ekbatan Dam, Kooshkabad and Marianaj. The uncertainties of this model are also analyzed. In the next step, the precipitation values of the mentioned 4 synoptic stations were predicted for 5 years monthly and annually with the help of seasonal autocorrelated moving average (SARIMA) and autocorrelated moving average (ARIMA) models, and finally, using the predicted rainfall values, the underground water level was predicted monthly and annually using the SVM-SA model for 5 years.
Result: The estimated values of underground water level were analyzed with the help of SVM-SA model using the indices of explanation coefficient (R2), root mean square error (RMSE) and Nash Sutcliffe coefficient (NSE). The results indicate that there is no significant difference between the performance of the model in predicting the underground water level in annual and monthly periods. But the SVM-SA model with Nash Sutcliffe coefficient of 0.993, root mean standard error of 0.417 and explanatory coefficient of 0.993 in the calibration period has been more accurate in monthly estimation of underground water level. In the next step, in order to achieve the best SARIMA and ARIMA models for predicting monthly and annual rainfall values, statistical indicators of coefficient of explanation (R2), root mean square error (RMSE), mean standard error (SE) and goodness of fit (AIC) are used. Finally, by using the ranks of the selected models according to the evaluation indices for monthly and annual periods for Aghkahriz station, respectively SARIMA(3,0,1)*(1,0,1) and ARIMA(3,0,2), for Ekbatan dam station according to SARIMA(1,0,1)*(1,1,2) and ARIMA(3,1,3), for Kooshkabad station according to SARIMA(1,1,3)*(1,1,1) and ARIMA(2,0,3) and for Marianaj station by SARIMA(1,0,1)*(1,1,2) and ARIMA(3,0,2) respectively, rainfall values for 5 years in monthly and annually forecast it placed. Finally, using the forecasted rainfall values with the help of SARIMA and ARIMA models, the groundwater level was forecasted monthly and annually for the next 5 years using the SVM-SA model.
Conclusion: One of the important results of this study is the absence of a significant relationship between the decrease in rainfall and the sharp drop in groundwater in the Hamedan-Bahar plain. In fact, the results of this research indicate that the sharp drop in the underground water level is caused by the excessive extraction of these valuable resources.
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