Groundwater Level Prediction of Ajabshir Plain using Fuzzy Logic, Neural Network Models and Time Series

Groundwater system studies to understanding its behavior, requires the exploratory drilling wells, pumping test and geophysical experiments, which can carried out with most cost. For this reason, simulation of groundwater flows by mathematical and computer models, which is an indirect method to groundwater studies, is being spent a few costs. In this research, the efficiency of artificial neural network, fuzzy logic and time series models has been investigated in groundwater level estimation of Ajabshir plain. Parameters of precipitation, temperature, flow rate and water level within time period of the previous month were used as input and the water table in each period were selected as output through monthly scale (2007-2018). To evaluating the performance of models, Correlation coefficient, root mean square error and coefficient of mean absolute error were used. The results showed that the Fuzzy Logic model are able to estimate water levels with acceptable accuracy. Gaussian functions was a membership functions of fuzzy model in groundwater level prediction, which fitted to the classified data and the output membership function of the sageno model is a function which is based on the inputs. In terms of accuracy, fuzzy logic model with the highest correlation coefficient (0.96), lowest root mean square error (0.068 m) and mean absolute error (0.056 m) was recognized as a best the model in the groundwater level prediction.