Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems\n(WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there\nis growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve\nthis purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and\nsocial aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies\nthree algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-\nOrganizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by\nPCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a\ncorrelation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to\nimprove the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day\nappear to be the most important predictive variables to build an accurate regression model.
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