Forecasting Water Demand in Residential, Commercial, and Industrial Zones in Bogotá, Colombia, Using Least-Squares Support Vector Machines

Abstract

The Colombian capital, Bogotá, has undergone massive growth in a short period of time. Naturally, this growth has increased the city’s water demand. The prediction of this demand will help understand and analyze consumption behavior, thereby allowing for effective management of the urban water cycle. This paper uses the Least-Squares Support Vector Machines (LS-SVM) model for forecasting residential, industrial, and commercial water demand in the city of Bogotá. The parameters involved in this study include the following: monthly water demand, number of users, and total water consumption bills (price) for the three studied uses. Results provide evidence of the model’s accuracy, producing R2 between 0.8 and 0.98, with an error percentage under 12%.