title: Forecasting urban water demand: A meta-regression analysis.
abstract: Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike.
authors.affiliation: Faculty of Economics and Management of Sousse & LaREMFiQ Research Laboratory, University of Sousse, Tunisia. Electronic address: maamer.sebri@gmail.com.
authors.forename: Maamar
authors.initials: M
authors.lastname: Sebri
mesh_terms: D002947:Cities; D005544:Forecasting; D008962:Models, Theoretical; D012044:Regression Analysis; D014481:United States; D014881:Water Supply
keywords: Accuracy; Box-Cox transformation; Forecasting; Mean absolute percentage error (MAPE); Meta-analysis; Urban water demand