Smart meters installed at the user-level provide a new data source for managing water
infrastructure. This research explores the use of machine learning methods, including …

This paper presents an artificial neural network-based model of domestic water
consumption. The model is based on real-world data collected from smart meters, and …

Regression Tree (RT) forecasting models are widely used in short-term demand forecasting.
Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and …

Abstract Knowledge of when, how, and by whom water is being used is crucial for planning
ways to conserve drinking water. The goal of this paper is to identify groups of similar …

The most important factor in planning and operating water distribution systems is satisfying
consumer demand. This means continuously providing users with quality water in adequate …

Abstract Knowledge on the determinants and patterns of water demand for different
consumers supports the design of customized demand management strategies. Smart …

This paper presents the development of a dynamic artificial neural network model (DAN2)
for comprehensive urban water demand forecasting. Accurate short-, medium-, and long …

In recent years, scarce water resources became one of the main problems that endanger
human species existence and the advancement of any nation. In this research, smart water …

Forecasts of water use are crucial to efficiently manage water utilities to meet growing
demand in urban areas. Improved household-level forecasts may be useful to water …

Water demand forecasting is a crucial task in the efficient management of the water supply
system. This paper compares classical and adapted machine learning algorithms used for …