I.J. Intelligent Systems and Applications, 2019, 9, 40-55 Published Online September 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2019.09.05 Copyright © 2019 MECS I.J. Intelligent Systems and Applications, 2019, 9, 40-55 Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model Mohammed Awad Department of Computer Systems Engineering, Arab American University, Palestine E-mail: mohammed.awad@aaup.edu Mohammed Zaid-Alkelani Department of Computer Science, Arab American University, Palestine E-mail: mzeid@qou.edu Received: 19 March 2019; Accepted: 09 May 2019; Published: 08 September 2019 Abstract—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. Index Terms—Prediction, Future Water Demand, Multilayer Perceptron NNs, Levenberg Marquardt Algorithm, Radial Basis Function NNs, Genetic Algorithms, ARIMA. I. INTRODUCTION The majority of the countries in the Middle East are suffering problems the increasing demand for water in light of the scarcity of resources to obtain sufficient quantities and satisfy the needs of citizens of different needs in different fields [1]. In general, the water demand and supply depends on the infrastructure of supply, distribution systems, and future strategic plans that have the capacity to meet the needs and sustain the success of the development [2]. So we can describe the Water Demand Forecasting as a total amount of used water, measured or predicted based on a certain application to know the general trend of consumption so as to evaluate the ability of existing resources to meet future needs within a geographic area and to provide the basis for planning future system and improve it to limit the uncertainties for future demand. The water sector is an important sector of sustainable development at the national level. The high demand for water and the significant gap between demand and supply in the water sector is one of the major challenges facing the sector over the next few years. The water demand is increasing because of natural population growth and national development requirements. This is a great challenge, and it is necessary to find creative solutions to supply the necessary quantities of water to different sectors and achieve balance for supply optimal water in Palestine. The existing models and applications that can predict the water demand effectively is a useful element in strategic planning and the processes of scheduling, maintenance [3]. Prediction strategies of water demand are very important to support and help the water authorities and municipalities in identifying future needs and to develop the necessary plans to find real solutions. The water circumstance in northern Palestine, such as the city of Jenin, is similar to the rest of Palestine cities. But in Jenin city, there are more