Mohammed Zaid-Alkelani

Work place: Department of Computer Science, Arab American University, Palestine



Research Interests: Computer systems and computational processes, Artificial Intelligence, Computer Networks, Database Management System


Mohammad Zaid Keelani received the B.S. Degree in Computer information systems from Al-Quds Open University in 2002, and the Master Degree in computer science at Arab American University in January 2019. From 2004 till now, he is registrar and developer at admission and registration Department, teacher assistant in Al-Quds Open University. His research interests include Artificial Intelligence, computer networks, Information system design, and Database management systems.

Author Articles
Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

By Mohammed Awad Mohammed Zaid-Alkelani

DOI:, Pub. Date: 8 Sep. 2019

The prediction of future water demand will help water distribution companies and government to plan the distribution process of water, which impacts on sustainable development planning. In this paper, we use a linear and nonlinear models to predict water demand, for this purpose, we will use different types of Artificial Neural Networks (ANNs) with different learning approaches to predict the water demand, compared with a known type of statistical methods. The dataset depends on sets of collected data (extracted from municipalities databases) during a specific period of time and hence we proposing a nonlinear model for predicting the monthly water demand and finally provide the more accurate prediction model compared with other linear and nonlinear methods. The applied models capable of making an accurate prediction for water demand in the future for the Jenin city at the north of Palestine. This prediction is made with a time horizon month, depending on the extracted data, this data will be used to feed the neural network model to implement mechanisms and system that can be employed to predicts a short-term for water demands. Two applied models of artificial neural networks are used; Multilayer Perceptron NNs (MLPNNs) and Radial Basis Function NNs (RBFNNs) with different learning and optimization algorithms Levenberg Marquardt (LM) and Genetic Algorithms (GAs), and one type of linear statistical method called Autoregressive integrated moving average ARIMA are applied to the water demand data collected from Jenin city to predict the water demand in the future. The execution results appear that the MLPNNs-LM type is outperformed the RBFNN-GAs and ARIMA models in the prediction the water demand values.

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