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Advisor(s)
Abstract(s)
Water scarcity is a global concern due to population growth,
climate change, and industrialization. Accurate water consumption simulation
and forecasting are essential for understanding consumption patterns
and predicting future demand. The control and visualization of
how different aspects such as precipitation, season, and population affect
water consumption can be a way for public agencies to plan actions that
minimize waste and assist in the correct use of water. Technology, and
especially Machine Learning and Digital Twin, can be used as tools for
this. In light of this, this project aims to develop a system for simulating
and forecasting water consumption in the Bragan¸ca region using
a Digital Twin. In order to accomplish this, a comprehensive analysis
is conducted to determine the necessary requirements for designing the
system. This analysis encompasses the evaluation of hardware, software,
data, machine learning models, web interface, as well as security and performance
requirements. Furthermore, the architecture of this system and
how it will be configured is analyzed, proposing a system with Training
Data Sources, Training Process, Updated Data Sources, Digital Twin,
Web Interface and Monitoring System. The system described in this
article is under development and it is hoped to achieve as a result the
full design of the Digital Twin and User Interface systems.
Description
Keywords
Water Consumption Simulator Web
Citation
Galvão, Matheus; Rici, Pedro; Lopes, Rui Pedro (2024). Digital Twin for Regional Water Consumption Simulation and Forecasting. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 333–346. ISBN 978-3-031-53024-1.
Publisher
Springer Nature