Water consumption forecasting using soft computing – a case study, Trinidad and Tobago
Author(s) -
Lee P. Leon,
Barkha Chaplot,
Akil Solomon
Publication year - 2020
Publication title -
water science and technology water supply
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 39
eISSN - 1607-0798
pISSN - 1606-9749
DOI - 10.2166/ws.2020.273
Subject(s) - water scarcity , consumption (sociology) , scarcity , water consumption , gene expression programming , water resources , soft computing , environmental science , water resource management , water use , climate change , hydrology (agriculture) , computer science , engineering , economics , ecology , machine learning , social science , sociology , artificial neural network , biology , microeconomics , geotechnical engineering
Water scarcity is one of the world’s fastest growing epidemics. Therefore, to combat it or mitigate the risks one must first understand how water is being consumed. This study focuses on the analysis of domestic water consumption with reference to how much of it is being consumed. Additionally, the study aims to propose an applicable and consistent method to forecast urban water consumption by using soft computing techniques. The investigation highlights the hourly, daily and monthly water consumption levels as well as the relationship between climate change and water demand using gene expression programming (GEP). The results of the study are relatively promising as it demonstrates that GEP can predict water consumption incorporating seasonal changes of wet and dry periods.
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