Urban Water Demand Forecasting and Comparative Analysis by Artificial Neural Networks, Support Vector Machines and Box-Jenkins Methods

Water demand forecasting is currently being used in many fields such as the investment planning, the design of the water systems (treatment plants, storage, transmission and distribution lines), the operation of existing systems at optimal capacity, calculation of operation and investment costs, and determination of urban water management policies (pricing policy, water conservation, etc.). Therefore, it can be said that an accurate water demand forecast has a key role in the planning, design, operation, and management of water systems. In this study, it is aimed to forecast Diyarbakir city centre drinking water demand by using Artificial Neural Networks method and Winters’s Seasonal Exponential Smoothing and Box-Jenkins methods based on time series analysis, and to compare forecasts obtained. For this purpose, firstly the data related to the variables affecting the water demand of Diyarbakir city centre for the time interval of 2003 – 2013 has been collected and analyzed. Then, a drinking water demand forecast has been made on the basis of this data by using Artificial Neural Network, Winters’s Seasonal Exponential Smoothing, and Box-Jenkins methods. The forecasts obtained from these three methods have been compared according to Productivity, The Mean Square Error, The Root Mean Square Error and The Mean Absolute Percentage Error criteria. In comparison results, it was seen that, in all performance criteria, Artificial Neural Networks method has better forecast results than those methods based on time series analysis.