Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach

• Published in: Water science and technology 2009-01, Vol.60 (12), p.3051-3059
• Main Authors: Zaqoot, Hossam Adel, Ansari, Abdul Khalique, Unar, Mukhtiar Ali, Khan, Shaukat Hyat
• Format: Article
• Language: English
• Subjects: Adaptability; Artificial neural networks; Basis functions; Biodegradation; Coastal environments; Coastal waters; Datasets; Dissolved oxygen; Environmental impact; Environmental Monitoring - methods; Environmental Monitoring - statistics & numerical data; Forecasting; Human influences; Mathematical models; Mediterranean Sea; Middle East; Modelling; Models, Chemical; Multilayer perceptrons; Neural networks; Neural Networks (Computer); Oxygen; Oxygen - analysis; Pollution control; Predictions; Radial basis function; Seawater; Seawater - chemistry; Semi-enclosed seas; Solubility; Turbidity; Water pollution; Water resources; Water temperature; Wind speed
• ISSN: 0273-1223; 1996-9732
• DOI: 10.2166/wst.2009.730

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.