Graphical Abstract • A new methodology is presented for development of water demand forecasting models • An iterative Bayesian method is used to diagnose and reduce model structural error • Statistical coverage of the prediction bounds is used to evaluate model performance • Heavy tailed error model better captures time varying nature of demand uncertainty • The methodology is suitable for use in real-time management of water supply systems 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.
In REF , the authors described a methodology to quantify, diagnose, and reduce not only structural modeling, but also errors in predictions for water forecasting models.
A probabilistic methodology for quantifying, diagnosing and reducing model structural and predictive errors in short term water demand forecasting
Environ. Model. Softw.
Environ. Model. Softw.
Computer Science