Forecasting Urban Water Demand Using Cellular Automata

• Main Authors: Laís Marques de Oliveira, Samíria Maria Oliveira da Silva, Francisco de Assis de Souza Filho, Taís Maria Nunes Carvalho, Renata Locarno Frota
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Water
• Subjects: water demand; cellular automata; dynamic modeling
• Online Access: https://www.mdpi.com/2073-4441/12/7/2038

Associating the dynamic spatial modeling based on the theory of cellular automata with remote sensing and geoprocessing technologies, this article analyzes what would be the per capita consumption behavior of Fortaleza-CE, located in the Northeast of Brazil, in 2017, had there not been a period of water scarcity between 2013 and 2017, and estimates the future urban water demand for the years 2021 and 2025. The weight of evidence method was applied to produce a transition probability map, that shows which areas will be more subject to consumption class change. For that, micro-measured water consumption data from 2009 and 2013 were used. The model was validated by the evaluation of diffuse similarity indices. A high level of similarity was found between the simulated and observed data (0.99). Future scenarios indicated an increase in water demand of 6.45% and 10.16% for 2021 and 2025, respectively, compared to 2017. The simulated annual growth rate was 1.27%. The expected results of urban water consumption for the years 2021 and 2025 are essential for local water resources management professionals and scientists, because, based on our results, these professionals will be able to outline future water resource management strategies.