Year: 2024
Type: conference-paper
Abstract: Relationships between water demand, pressure, and leakage highlight the need for accurate supply to match demand. This study addresses the challenges of forecasting short-term water demand and was part of the Battle for Water Demand Forecasting competition involving 10 real-world District Metered Areas in Italy. A nine-layer convolutional neural network model was proposed that considers demand from previous time steps, time of the day, weather conditions, day type, and other deterministic temporal factors to predict water demand. Bayesian optimization was used for hyperparameter tuning. The model can predict and forecast short-term water demand with reasonable accuracy.
Authors: Faten Ayyash, Matthew Hayslep, Taegon Ko, Mulenga Kalumba, Kondwani Simukonda, Raziyeh Farmani
Institutions: University of Exeter, University of Zambia
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Funder: Schlumberger Foundation
OpenAlex ID: W4402495901