Work

Water Demand Forecasting with Multi-Objective Computational Intelligence

Year: 2024

Type: conference-paper

Abstract: With increasing pressures from population growth, urbanization, and climate change, effective water resource management is crucial.This paper presents a computational intelligence framework employing machine learning and multi-objective optimization for the short-term forecasting battle of urban water demand within District Metered Areas (DMAs).Our methodology utilizes historical data from DMAs in North-East Italy, focusing on daily and weekly forecasts to optimize water utility operations and energy purchasing.By integrating environmental variables, the proposed models aim to improve forecasting accuracy, model interpretability, and structural complexity, thus meeting the practical needs of water utilities.

DOI: 10.3390/engproc2024069079

Authors: Gilberto Reynoso-Meza, Elizabeth Pauline Carreño-Alvarado

Institutions: Pontifícia Universidade Católica do Paraná, Universidade Federal do Paraná

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Sustainable cities and communities

Open Access status: gold

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico

Awards: G110145737, G736087426