Hydraulic Modeling of the Water Resources using Learning Techniques

Quantitative and qualitative analysis of water resources has become one of the most widely used topics in water resources research today. In this research, data mining, artificial intelligence, mathematical techniques have been used to simulate water behavior and estimate its parameters changes. The models used to estimate hydrogeological parameters are Self-adaptive Extreme learning machine (SAELM), Least square support vector machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression (MLR) models. Also, to evaluate the performance of these models, the accuracy of the models was assessed in the form of 5 approaches. The results showed that the SAELM model was the best model based on the simulation and correlation diagrams. Based on accuracy evaluation indices, the SAELM model with RMSE, MAPE and, R indices equal to 0.1545, 0.0070, and 0.9979, respectively, had the highest accuracy in hydrogeological parameters prediction. Based on Uncertainty Analysis by the Wilson Score method, the performance of the top model (SAELM) was estimated to be underestimated. Also, based on the error ratio diagrams, the most accurate results were related to the SAELM model. Finally, the SAELM model was assigned the lowest error rate using the error distribution diagrams.