Comparison of the performance of Artificial Neural Networks and Gene Expression to predict the groundwater level in arid and semi-arid areas(Case study: Jiroft plain)

Modeling and prediction of groundwater level is one of the basic tasks to achieve optimal management of water resources. One way to predict the groundwater level is using artificial intelligence techniques such as neural networks and gene expression planning. The aim of this study was to evaluate the effectiveness of artificial neural network (ANN) and gene expression methods in predicting groundwater level of Jiroft plain aquifer. For this purpose, the data from 65 piezometric wells in the Jiroft plain aquifer was used for a period of eleven years. The level of piezometric wells by each of the techniques of gene expression and neural network were simulated separately and at the end, the root mean square, mean absolute error, and R2 were used to determine the accuracy of the predictions of each of the methods.The results of this study showed the higher efficiency and accuracy of both neural network techniques and gene expression in predicting the groundwater level region. The correlation coefficient in the artificial neural network method gene expression method was equal to 0.96 and 0.72, respectively, indicating the higher efficiency of artificial neural network in the simulation of Jiroft plain groundwater data over the period studied.