Correlation Analysis of Water Demand and Predictive Variables for Short‐Term Forecasting Models

Abstract

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

Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

“…Brentan et al [47] applied PCA, SOM, and random forest to predict water demand. The study was conducted in three metropolitan areas of France and a Brazilian city, exploring climatic and social variables to improve the knowledge of the residential demand for water.…”

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

“…For example, from the study of associations between exposure to such elements as air pollution, weather variables or pollen, important results regarding ways to show disease symptoms and consequences may be derived [55,56]. In water supply, similar associations show, for instance, how high temperatures impact on progressively producing higher levels of water demand for a certain population [57]. As a consequence, it is useful to investigate cause-effect relationships between exogenous variables and levels of water demand.…”

A Greedy Sampling Design Algorithm for the Modal Calibration of Nodal Demand in Water Distribution Systems

“…In practice, the number of the sensors is less than the number of nodal consumer demand. Thus, model calibration is an ill-conditioning problem [27][28][29], which will result IN the parameter uncertainty [30]. For the nodes (pipes) with the sensors, we can minimize (1) so that the prediction value of the calibrated-model fits the measured value, while for nodes with no sensors, the prediction value may deviate from true values [29].…”