Zusammenfassung
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 …
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