Urban Hourly Water Demand Prediction Using Human Mobility Data

The efficient management of a water supply system requires precise water demand forecasts as inputs. This paper compares existing prediction methods and improves their performance by integrating human-related factors with water consumption in an urban area. Furthermore, a framework for processing and transforming mobility data into time-series is presented. Results show that using human mobility data improves forecasting accuracy reaching 87.6%.