Probabilistic forecasting;
Drinking water treatment;
Nonlinear regression;
Water level prediction;
Data-driven modelling;
WATER-LEVEL FLUCTUATIONS;
NEURAL-NETWORKS;
CLIMATE-CHANGE;
LAKE ERIE;
RISK;
QUALITY;
SYSTEM;
MODEL;
TOOL;
DAM;
D O I:
10.1016/j.jclepro.2017.04.003
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
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. (C) 2017 Elsevier Ltd. All rights reserved.