A Probabilistic Methodology for Quantifying, Diagnosing and Reducing Model Structural and Predictive Errors in Short Term Water Demand Forecasting

Christopher J Hutton, Zoran Kapelan

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)

Abstract

Accurate forecasts of water demand are required for real-time control of water supply systems under normal and abnormal conditions. A methodology is presented for quantifying, diagnosing and reducing model structural and predictive errors for the development of short term water demand forecasting models. The methodology (re-)emphasises the importance of posterior predictive checks of modelling assumptions in model development, and to account for inherent demand uncertainty, quantifies model performance probabilistically through evaluation of the sharpness and reliability of model predictive distributions. The methodology, when applied to forecast demand for three District Meter Areas in the UK, revealed the inappropriateness of simplistic Gaussian residual assumptions in demand forecasting. An iteratively revised, parsimonious model using a formal Bayesian likelihood function that accounts for kurtosis and heteroscedasticity in the residuals led to sharper yet reliable predictive distributions that better quantified the time varying nature of demand uncertainty across the day in water supply systems.
Original languageEnglish
Pages (from-to)87-97
JournalEnvironmental Modelling and Software
Volume66
DOIs
Publication statusPublished - 2015

Fingerprint Dive into the research topics of 'A Probabilistic Methodology for Quantifying, Diagnosing and Reducing Model Structural and Predictive Errors in Short Term Water Demand Forecasting'. Together they form a unique fingerprint.

Cite this