DEVELOPMENT AND EVALUATION OF AN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR THE CALCULATION OF SOIL WATER RECHARGE IN A WATERSHED

Abstract

Modeling of groundwater recharge is one of the most important topics in hydrology due to its essential application to water resources management. In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) method is used to simulate groundwater recharge for watersheds. In-situ observational datasets for temperature, precipitation, evapotranspiration, (ETo) and groundwater recharge of the Lake Karla, Thessaly, Greece watershed were taken into consideration for the present study. The datasets consisted of monthly average values of the last almost 50 years, where 70% of the values used for learning with the rest for the testing phase. The testing was performed under a set of different membership functions without expert’s knowledge acquisition and with the support of a five-layer neural network. Experimental verification shows that, the 3-3-3 combination under the trapezoid membership function with the hybrid neural network support and the 2-2-2 combination under the g-bell membership function with the same neural network support perform the best among all combinations with RMSE 4.78881 and 4.12944 giving on average 5% deviation from the observed values.