Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok

Abstract

The water demand of a city is a complex and non linear function of climatic, socioeconomic, institutional and management variables. Identifying the prominent variables among these is essential in order to adequately predict water demand, and to plan and manage water resources and the supply systems. Further, the need for such identification becomes more pronounced when data constraints arise. The objective of this study was to establish, using correlation and sensitivity analyses, a minimum set of variables required to predict water demand with significant accuracy. Artificial Neural Networks (ANN) models were developed to predict short-term (daily) and medium-term (monthly) demands for Bangkok. Using meteorological and water utility variables for short-term prediction, and different ANN architecture, 16 sets of models with a 1-, 2-and 3-day lead period were developed. Although the best fit models for the three lead periods used different input variables, prediction accuracies over 98% were achieved by using only the historic daily demand (HDD) as the explanatory variable. Similarly, for medium-term prediction, 11 sets of models with lead periods of 1-, 2-and 6-months were developed, using meteorological, water utility and socioeconomic variables. The best fit models for the three lead periods used all explanatory variables but prediction accuracies of more than 98% were obtained by downsizing the variable set. The meteorological variables have a greater influence on medium-term prediction as compared to short-term prediction, suggesting that future water demand in Bangkok could be significantly affected by climate change.

Learning-based tuning of supervisory model predictive control for drinking water networks

“…3 top) with 100 neurons in the hidden layer. The inputs to the neural model are chosen based on literature review (Babel and Shinde, 2011) and correlation analysis, considering consumption data and meteorological variables such as temperature T and air relative humidity RH. Principal component analysis (PCA) preprocessing is applied to the training patterns in order to reduce the dimension of the input vectors and to obtain uncorrelated values that facilitate the learning process.…”

Adaptive multilevel neuro-fuzzy Model Predictive Control for Drinking Water Networks

“…It minimizes a combination of squared errors and weights, and then determines the correct combination so as to produce a network that generalizes well. The inputs to the forecasting models are chosen based on literature review [22], and correlation analysis, considering consumption data and meteorological variables such as temperature and air relative humidity. Principal component analysis (PCA) preprocessing is applied to the training patterns.…”

A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

“…Finally, as far as approach is concerned, many of the models recently proposed in the literature are based on data-driven techniques such as artificial neural networks (ANNs) (e.g., [12,17,[22][23][24][25], support vector machines (SVMs) (e.g., [26,27], fuzzy logic [28], projection pursuit regression (PPR), random forests (RMs) and multivariate adaptive regression splines (MARS) [12].…”