Groundwater Level Prediction Using Hybrid Artificial NeuralNetwork with Genetic Algorithm

In recent years, the growth of the economy has led to the increasing exploitation of water resources and groundwater. Due to heavy abstraction of groundwater its importance increases, with the requirements at present as well as in future. Accurate estimates of groundwater level have a valuable effect in improving decision support systems of groundwater resources exploitation. This paper investigates the ability of a hybrid model of artificial neural network (ANN) and genetic algorithm (GA) in predicting groundwater levels in an observation well from Udupi district. The ground water level for a period of ten years and rainfall data for the same period is used to train the model. A standard feed forward network is utilized for performing the prediction task. A groundwater level forecasting model is developed using artificial neural network. The Genetic Algorithm is used to determine the optimized weights for ANN. This study indicates that the ANN-GA model can be used successfully to predict groundwater levels of observation well. In addition, a comparative study indicates that the ANN-GA hybrid model performs better than the traditional ANN back-propagation approach.