Hydrograph model representing aquifer with the model of the Madflow and meta model neural-fuzzy network simulator (case study)

Decreasing atmospheric precipitation, limiting water resources and increasing drainage of groundwater has led to a reduction in the surface of the plain, and therefore an underground water map is an effective tool for managing and protecting these resources. In this study, the monthly statistical data of the surface of piezometers for 5 years water (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, using the concept of groundwater of the Modflow and supermodel of the neuro-fuzzy simulator, the hydrograph represented the aquifer modeling and the results were compared. The results showed that the concept model of Modflow with a coefficient of explanation of 0.736 in the test phase compared to the neuro-fuzzy simulator model with a coefficient of explanation of 0.6348 has better performance.