Performance of an Artificial Neural Network model for simulating saltwater intrusion process in coastal aquifers when training with noisy data

Abstract

This paper evaluates the performance of an Artificial Neural Networks (ANN) model for approximating density depended saltwater intrusion process in coastal aquifer when the ANN model is trained with noisy training data. The data required for training, testing and validation of the ANN model are generated using a numerical simulation model. The simulated data, consisting of corresponding sets of input and output patterns are used for training a multilayer perception using back-propagation algorithm. The trained ANN predicts the concentration at specified observation locations at different time steps. The performance of the ANN model is evaluated using an illustrative study area. These evaluation results show the efficient predicting capabilities of an ANN model when trained with noisy data. A comparative study is also carried out for finding the better transfer function of the artificial neuron and better training algorithms available in Matlab for training the ANN model.