APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTING pH IN SEAWATER ALONG GAZA BEACH

Coastal water issues are gaining worldwide attention because of their impact on health and other environmental problems. This article is concerned with the comparison between artificial neural networks and statistical methods to predict the degree of acidity (pH) in the coastal waters along the Gaza beach. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are trained and developed with reference to three parameters (water temperature, wind velocity, and turbidity) to predict the level of pH in the seawater. Both networks were developed using the combination of the data collected from nine sites over a period of 4 years, including 294 samples for training and 90 samples for testing the performance of models. The results show that the MLP and RBF models have good ability to predict the pH level. Each network's performance was tested with different sets of data, and the results show satisfactory performance. Results of the developed networks were compared with the statistical regression method and found that the predictions of neural networks are better than the conventional methods. Predictions result show that artificial neural networks approach have good ability for the modeling of pH level in the coastal waters along Gaza beach. It is hoped that neural networks will prove to be a promising alternative to traditional methods used and can contribute in the improvement of the quality of seawater.