Mohammed Awad

Work place: Department of Computer Systems Engineering, Arab American University, Palestine



Research Interests: Computer systems and computational processes, Artificial Intelligence, Neural Networks, Computer Architecture and Organization, Data Structures and Algorithms, Analysis of Algorithms, Combinatorial Optimization


Mohammed Awad received the B.S. Degree in Industrial Automation Engineering from Palestine Polytechnic University in 2000, master & Ph.D. degrees in Computer Engineering from the Granada University Spain (both are Scholarship from Spanish Government). From 2005 to 2006, he was a contract Researcher at Granada University in the research group Computer Engineering: Perspectives and Applications. Since Feb. 2006, he has been Assistant Professor in Computer System Engineering Department, College of Engineering and Information Technology at Arab American University. At 2010 he has been Associate Professor in Computer Engineering. At 2016 he has been Full Professor in Computer Engineering. He worked for more than 13 years at the Arab American University in academic Position, in parallel with various Dean of Scientific Research and Editor-In-Chief, Journal of AAUJ). Through the research and educational experience, he has developed a strong research record. His research interests include Artificial Intelligence, Neural Networks, Function Approximation, and Complex Systems, Clustering Algorithms, Optimization Algorithms, and Time Series Prediction. He won a number of awards and research grants.

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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