Effectiveness of meta models of Gene Expression and Neural-Fuzzy Network Simulations in Hydrograph Modeling of Aquifer Representation

Underground water mapping is an effective tool for managing and protecting these resources, in order to apply a proper management to long-term planning and to better utilize the potential of the water in the plains. In this study, the monthly statistical data of the surface of piezometers for 5 years blue (89-88 to 93-92) related to the 8-pisometer level of the Lower-Andimeshk plain aquifer. At the beginning, using the Tesine method, the weighted average of each piezometer was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative water column of the study area, was calculated. Then, by using the neuro-fuzzy simulator and meta-model of the gene expression simulator, the hydrograph represents the modeling aquifer and the results were compared. The results showed that the meta-model of gene expression simulator with a coefficient of explanation of 7390.0 at the test stage was better than the neuro-fuzzy simulator model with a coefficient of explanation of 0.6348.Underground water mapping is an effective tool for managing and protecting these resources, in order to apply a proper management to long-term planning and to better utilize the potential of the water in the plains. In this study, the monthly statistical data of the surface of piezometers for 5 years blue (89-88 to 93-92) related to the 8-pisometer level of the Lower-Andimeshk plain aquifer. At the beginning, using the Tesine method, the weighted average of each piezometer was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative water column of the study area, was calculated. Then, by using the neuro-fuzzy simulator and meta-model of the gene expression simulator, the hydrograph represents the modeling aquifer and the results were compared. The results showed that the meta-model of gene expression simulator with a coefficient of explanation of 7390.0 at the test stage was better than the neuro-fuzzy simulator model with a coefficient of explanation of 0.6348.Underground water mapping is an effective tool for managing and protecting these resources, in order to apply a proper management to long-term planning and to better utilize the potential of the water in the plains. In this study, the monthly statistical data of the surface of piezometers for 5 years blue (89-88 to 93-92) related to the 8-pisometer level of the Lower-Andimeshk plain aquifer. At the beginning, using the Tesine method, the weighted average of each piezometer was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative water column of the study area, was calculated. Then, by using the neuro-fuzzy simulator and meta-model of the gene expression simulator, the hydrograph represents the modeling aquifer and the results were compared.