Enhancing Water Management: A Comparative Analysis of Time Series Prediction Models for Distributed Water Flow in Supply Networks

Abstract

Water scarcity poses a significant challenge to social integration and economic development, necessitating efficient water management strategies. This study compares time series forecasting models, both classical, Holt–Winters and ARIMA, and modern, LSTM and Prophet, to determine the most accurate model for predicting water flow in public supply networks. Data from four rural Portuguese locations were used, with preprocessing ensuring quality and uniformity. Performance metrics were evaluated for both medium-term (10 days) and long-term (3 months) forecasts. Results indicate that classical models like Holt–Winters and ARIMA perform better for medium-term predictions, while modern models, particularly LSTM, excel in long-term forecasts by effectively capturing seasonal patterns. Future research should integrate additional variables and explore hybrid models to enhance forecasting accuracy.

Keywords

• time series
• prediction models
• water flow distribution
• Holt–Winters
• ARIMA
• LSTM