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

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.

Design Thinking for Innovation in Sustainable Built Environments and the Integration of an Inclusive Foresight and Design Thinking Framework

“…The existing context and solutions encompass several issues: (1) The prediction of the range of household water consumption in Huelva, Andalusia, Spain. The article accessed uses a web-based prototype within a DT framework based on a human-centered methodology to engage households and mitigate the risks associated with development and adoption [31]; (2) The case of a sustainable waste sterilization system in the Piedmont Region, Italy, using a DT framework to develop a new method for the treatment and disposal of infectious waste [34]; (3) Sustainable practices developed from DT and social practice theories, aiming to create a toolkit to design interventions that unlock unsustainable practices [36].…”

Forecasting water demand for Istanbul by applying different machine learning algorithms

“…As part of a project, Benítez et al (2019) needed water demand estimation to prevent leaks that may occur in the network line and applied a machine learning algorithm. Bermejo-Martín et al (2021) tried to create water consumption classes for the water consumer groups they obtained earlier and used Classification and Regression Trees to apply them to the water consumption range. Buck et al (2020) conducted a criterion determination study to evaluate the performance of algorithms used to estimate daily water consumption in the literature.…”