Medium-term storage volume prediction for optimum reservoir management: A hybrid data-driven approach. (15th June 2017)

• Record Type: Journal Article
• Authors: Bertone, Edoardo; O' Halloran, Kelvin; Stewart, Rodney A.; de Oliveira, Guilherme F.
• Is Part Of: Journal of cleaner production. Volume 154(2017)
• Page Start: 353
• Page End: 365
• Publication Date: 2017-06-15
• Subjects: Probabilistic forecasting -- Drinking water treatment -- Nonlinear regression -- Water level prediction -- Data-driven modelling; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.04.003
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: A hybrid regressive and probabilistic model was developed that is able to forecast, six weeks ahead, the storage volume of Little Nerang dam. This is a small elevated Australian drinking water reservoir, gravity-fed to a nearby water treatment plant while a lower second main water supply source (Hinze dam) requires considerable pumping. The model applies a Monte Carlo approach combined with nonlinear threshold autoregressive models using the seasonal streamflow forecasts from the Bureau of Meteorology as input and it was validated over different historical conditions. Treatment operators can use the model for quantifying depletion rates and spill likelihood for the forthcoming six weeks, based on the seasonal climatic conditions and different intake scenarios. Greater utilization of the Little Nerang reservoir source means a reduced supply requirement from the Hinze dam source that needs considerable energy costs for pumping, leading to a lower cost water supply solution for the region. Highlights: Hybrid nonlinear regression and probabilistic model for storage volume forecasting. Seasonal streamflow forecasts and expected outflows are the main inputs. Quantification of depletion/spill risks helps maximize the reservoir's intake. Maximum intake from this reservoir implies treatment and energy costs reduction.