TY - JOUR ID - 72429 TI - Estimation of Quantity and Quality Parameters of Groundwater Using Numerical Models (Case Study: Mighan Desert Basin, Arak) JO - Iranian Journal of Soil and Water Research JA - IJSWR LA - en SN - 2008-479X AU - Poursaeid, Mojtaba AU - Mastouri, Reza AU - Shabanlou, Saeid AU - Najarchi, Mohsen AD - Department of Civil, Arak Branch, Islamic Azad University AD - Department of Civil Engineering, Islamic Azad University Arak Branch AD - Department of Water Engineering, Kermanshah Branch, Islamic Azad University AD - Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Islamic Azad University of Arak Branch, Arak Y1 - 2020 PY - 2020 VL - 51 IS - 1 SP - 201 EP - 216 KW - salinity KW - Electrical conductivity KW - Groundwater level KW - MODFLOW KW - Extreme learning machine DO - 10.22059/ijswr.2019.279388.668166 N2 - In this paper, salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, were simulated using four novel artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (OSELM) as well as the MODFLOW software (MT3D model). In order to develop the hybrid artificial intelligence models, the wavelet transform was employed. First, the effective lags in estimating the quality and quantity parameters of the groundwater were identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models were developed using the effective lags for each of the artificial intelligence methods. Then, the superior models in simulating the groundwater quality and quantity parameters were detected by conducting a sensitivity analysis. Subsequently, the most effective lags in estimating these parameters were introduced. In addition, the results of The MODFLOW model were compared with the artificial intelligence models, and it was concluded that the latter were more accurate. For instance, the scatter index and Nash-Sutcliffe efficiency coefficient values for TDS simulation by the superior model were 5.34E-03 and 0.991, respectively. Additionally, RMSE and MAE for estimating groundwater level using the superior model were obtained 0.078 and 0.061, respectively. Finally, uncertainty analysis for the superior models was carried out. UR - https://ijswr.ut.ac.ir/article_72429.html L1 - https://ijswr.ut.ac.ir/article_72429_745c15fba65a2f38dc9b999ecb79ad74.pdf ER -