Despite recent efforts to apply machine learning (ML) for water demand modeling, overcoming the black-box nature of these techniques to extract practical information remains a challenge, especially in developing countries. This study integrated random forest (RF), self-organizing map (SOM), and artificial neural network (ANN) techniques to assess water demand patterns and to develop a predictive model for the city of Fortaleza, Brazil. We performed the analysis at two spatial scales, with different level of information: census tracts (CTs) at the fine scale, and census blocks (CBs) at the coarse scale. At the CB scale, demand was modeled with socioeconomic, demographic, and household characteristics. The RF technique was applied to rank these variables, and the most relevant were used to cluster census blocks with SOMs. RFs and ANNs were used in an iterative approach to define the input variables for the …
%0 Journal Article
%1 carvalho2021urban
%A Carvalho, Taís Maria Nunes
%A de Assis de Souza Filho, Francisco
%A Porto, Victor Costa
%D 2021
%I American Society of Civil Engineers
%J Journal of Water Resources Planning and Management
%K imported topic_earthenvironment
%N 1
%P 05020026
%T Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil
%V 147
%X Despite recent efforts to apply machine learning (ML) for water demand modeling, overcoming the black-box nature of these techniques to extract practical information remains a challenge, especially in developing countries. This study integrated random forest (RF), self-organizing map (SOM), and artificial neural network (ANN) techniques to assess water demand patterns and to develop a predictive model for the city of Fortaleza, Brazil. We performed the analysis at two spatial scales, with different level of information: census tracts (CTs) at the fine scale, and census blocks (CBs) at the coarse scale. At the CB scale, demand was modeled with socioeconomic, demographic, and household characteristics. The RF technique was applied to rank these variables, and the most relevant were used to cluster census blocks with SOMs. RFs and ANNs were used in an iterative approach to define the input variables for the …
@article{carvalho2021urban,
abstract = {Despite recent efforts to apply machine learning (ML) for water demand modeling, overcoming the black-box nature of these techniques to extract practical information remains a challenge, especially in developing countries. This study integrated random forest (RF), self-organizing map (SOM), and artificial neural network (ANN) techniques to assess water demand patterns and to develop a predictive model for the city of Fortaleza, Brazil. We performed the analysis at two spatial scales, with different level of information: census tracts (CTs) at the fine scale, and census blocks (CBs) at the coarse scale. At the CB scale, demand was modeled with socioeconomic, demographic, and household characteristics. The RF technique was applied to rank these variables, and the most relevant were used to cluster census blocks with SOMs. RFs and ANNs were used in an iterative approach to define the input variables for the …},
added-at = {2024-11-29T11:56:28.000+0100},
author = {Carvalho, Taís Maria Nunes and de Assis de Souza Filho, Francisco and Porto, Victor Costa},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/258089187cbc45bb3836388fc277762a9/joum576e},
citation = {Journal of Water Resources Planning and Management 147 (1), 05020026, 2021},
interhash = {7ff326285a05e3f9ca4e6347a8d429c3},
intrahash = {58089187cbc45bb3836388fc277762a9},
journal = {Journal of Water Resources Planning and Management},
keywords = {imported topic_earthenvironment},
number = 1,
pages = 05020026,
publisher = {American Society of Civil Engineers},
timestamp = {2024-11-29T11:56:28.000+0100},
title = {Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil},
volume = 147,
year = 2021
}
