Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Water Consumption Range Prediction in Huelva’s Households using Classification and Regression Trees

Version 1 : Received: 20 December 2020 / Approved: 22 December 2020 / Online: 22 December 2020 (09:42:57 CET)

How to cite: Bermejo-Martín, G.; Rodríguez-Monroy, C.; Nuñez-Guerrero, Y.M. Water Consumption Range Prediction in Huelva’s Households using Classification and Regression Trees. Preprints 2020, 2020120546 (doi: 10.20944/preprints202012.0546.v1). Bermejo-Martín, G.; Rodríguez-Monroy, C.; Nuñez-Guerrero, Y.M. Water Consumption Range Prediction in Huelva’s Households using Classification and Regression Trees. Preprints 2020, 2020120546 (doi: 10.20944/preprints202012.0546.v1).

Abstract

This paper uses the numerical results of surveys sent to Huelva’s (Andalusia, Spain) households to determine the degree of knowledge they have about the urban water cycle, needs, values, and attitudes regarding water in an intermediary city with low water stress. In previous research, we achieved three different households’ clusters. The first one grouped households with high knowledge of the integral water cycle and a positive attitude to smart devices at home. The second cluster described households with low knowledge of the integral water cycle and high sensitivity to price. The third one showed average knowledge and predisposition to have a closer relationship with the water company. This paper continues with this research line, applying Classification and Regression Trees (CART) to determine which hierarchy of variables/factors/ independent components obtained from the surveys are the decisive ones to predict the range of household water consumption in Huelva. Positive attitudes towards improved cleaning habits for personal or household purposes are the highest hierarchy component to predict the water consumption range. Second in the hierarchy, the variable Knowledge Global Score about the integral urban water cycle, associated with water literacy, also contributes to predicting the water consumption range. Together with the three clusters obtained previously, these results will allow us to design water demand management strategies (WDM) fit for purpose that enable Huelva’s households to use water more efficiently.

Subject Areas

classification and regression trees; CART algorithm; design thinking; web-based prototype; engagement; ICT technologies; households; water demand management (WDM); machine learning; water consumption range

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