Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model

Abstract

This paper presents the development of a dynamic artificial neural network model (DAN2) for comprehensive urban water demand forecasting. Accurate short-, medium-, and long-term demand forecasting provides water distribution companies with information for capacity planning, maintenance activities, system improvements, pumping operations optimization, and the development of purchasing strategies. We examine the effects of including weather information in the forecasting models and show that such inclusion can improve accuracy. However, we demonstrate that by using time series water demand data, DAN2 models can provide excellent fit and forecasts without reliance upon the explicit inclusion of weather factors. All models are validated using data from an actual water distribution system. The monthly, weekly, and daily models produce forecasting accuracies above 99%, and the hourly models above 97%. The excellent model accuracy demonstrates the effectiveness of DAN2 in forecasting urban water demand across all time horizons. Finally, we compare our results with those of an autoregressive integrated moving average model and a traditional artificial neural network model.