Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned factors, have been used to estimate River Water Withdrawal (RWW) for agricultural uses. Lavasanat and Qazvin are selected as study areas, located in the Namak-lake basin in Iran, with Bsk and Csa climate categories, respectively. Estimation of RWW is performed using single and Wavelet-hybrid (W-hybrid) data-driven methods, including Artificial Neural Networks (ANNs), Wavelet-ANN (WANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet-GEP (WGEP). Due to the evaluation criteria, the performance of the WGEP model is the best among others for estimating RWW variables in both study areas. Considering the W-hybrid models with data de-noising for estimating RWW in Lavasanat and Qazvin study areas, the obtained values of RMSE for WGEP11 to WGEP15 and WGEP21 to WGEP25 equal 67.268, 54.659, 80.871, 50.796, 15.676 and 105.532, 96.615, 105.018, 160.961, 44.332, respectively. The results indicate that WGEP and ANN are the best and poorest models in both study areas without regarding climate conditions effects. Also, a combined factor which includes River Width (RW), minimum flow rate (QMin), average flow rate (QMean), Electrical Conductivity (EC), and Cultivated Area (CA) variables, is introduced as the best factor to estimate RWW variables compared to the other factors in both Bsk and Csa climate categories. On the other hand, qualitative hydrological and land use factors were the weakest ones to estimate RWW variables in Bsk and Csa climate categories, respectively. Therefore, the current study explores that the mathematical relations for estimating RWW have a significant effect on water resources management and planning by policymakers in the future.
The River Water Withdrawal (RWW) for agricultural purposes was estimated using data-driven methods.
The impact of climatic condition, river morphology, quantitative and qualitative hydrological characteristics, and land use features on the RWW estimation was assessed.
De-noising the data and developing the combined factor could improve the model's performance.