Short term water demand forecast modelling using artificial intelligence for smart water management

Abstract

Water is an important resource for life and its existence. Water demand is increasing with increasing economic growth and population, while the water availability is continually depleting making an increasing stress on freshwater resources, necessitating monitoring of water consumption. In addition to controlling the water supply with an efficient water management system, automating the system in terms of both monitoring and operation has received a lot of attention in recent years. Short-term water demand forecast aids in the optimal control of a water supply system and its accurate forecasting helps in reducing operating costs and saving energy. Despite extensive research, the use of demand forecasting for efficient water management has yet to be implemented in India. As a result, the focus of this research is primarily on the model for forecasting short-term water demand using artificial intelligence. A comparative study has been carried out between nine machine learning and deep learning models using the water consumption data over the period from 2020 to 2021 for the city of Hubli in Karnataka. Univariate and multivariate time series forecasting models were considered using the 10-min interval flow meter readings to find the most suitable predictive model. For univariate time series forecasting, only the water consumption was used to predict the water demand, whereas, for the multivariate model, climatic parameters, and calendar inputs (like an hour of the day, holidays, etc.) were considered along with the water consumption data. The results suggest that the deep learning models outperformed the machine learning models, and Long-Short Term Memory (LSTM) model demonstrates the best prediction performance in the two scenarios with a mean absolute error of 0.11 m3/hr for univariate model and 2.96 m3/hr for the multivariate model. The best predictive model can be used to forecast the short-term water demand for any region to ensure sustainable water resource management.