A Neural Network for Water Level Prediction in Artesian Wells

The paper shows an application of neural networks for the prediction of water levels in artesian wells. The design of the neural network follows a systematic methodology, which can be used for a variety of prediction problems. A part of the design methodology is based on cross validation, which helped us in finding and correcting data anomalies due to different methods used for generating data. The final network is able to predict water level within the required tolerance, thus resulting in an effective decision support system to help managers in programming the exploitation of artesian wells in the short-term.