Medium-term storage volume prediction for optimum reservoir management: A hybrid data-driven approach

被引:14
作者
Bertone, Edoardo [1 ,2 ]
Halloran, Kelvin O. [3 ]
Stewart, Rodney A. [1 ,2 ]
de Oliveira, Guilherme F. [1 ]
机构
[1] Griffith Univ, Griffith Sch Engn, Gold Coast Campus, Southport, Qld 4222, Australia
[2] Griffith Univ, Cities Res Inst, Gold Coast Campus, Southport, Qld 4222, Australia
[3] Seqwater, Ipswich, Qld 4305, Australia
关键词
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.
引用
收藏
页码:353 / 365
页数:13
相关论文
共 52 条
[1]   Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods [J].
Altunkaynak, Abdusselam .
WATER RESOURCES MANAGEMENT, 2014, 28 (08) :2293-2314
[2]   Forecasting surface water level fluctuations of lake van by artificial neural networks [J].
Altunkaynak, Adduesselam .
WATER RESOURCES MANAGEMENT, 2007, 21 (02) :399-408
[3]   Fuzzy neural networks for water level and discharge forecasting with uncertainty [J].
Alvisi, Stefano ;
Franchini, Marco .
ENVIRONMENTAL MODELLING & SOFTWARE, 2011, 26 (04) :523-537
[4]  
Arfi Robert, 2003, Lakes & Reservoirs Research and Management, V8, P247, DOI 10.1111/j.1440-1770.2003.00223.x
[5]   Extreme events, water quality and health: A participatory Bayesian risk assessment tool for managers of reservoirs [J].
Bertone, Edoardo ;
Sahin, Oz ;
Richards, Russell ;
Roiko, Anne .
JOURNAL OF CLEANER PRODUCTION, 2016, 135 :657-667
[6]   Hybrid water treatment cost prediction model for raw water intake optimization [J].
Bertone, Edoardo ;
Stewart, Rodney A. ;
Zhang, Hong ;
O'Halloran, Kelvin .
ENVIRONMENTAL MODELLING & SOFTWARE, 2016, 75 :230-242
[7]   An autonomous decision support system for manganese forecasting in subtropical water reservoirs [J].
Bertone, Edoardo ;
Stewart, Rodney A. ;
Zhang, Hong ;
Bartkow, Michael ;
Hacker, Charles .
ENVIRONMENTAL MODELLING & SOFTWARE, 2015, 73 :133-147
[8]   Estimation of the Change in Lake Water Level by Artificial Intelligence Methods [J].
Buyukyildiz, Meral ;
Tezel, Gulay ;
Yilmaz, Volkan .
WATER RESOURCES MANAGEMENT, 2014, 28 (13) :4747-4763
[9]   FORECAST MODEL FOR GREAT LAKES WATER LEVELS [J].
COHN, BP ;
ROBINSON, JE .
JOURNAL OF GEOLOGY, 1976, 84 (04) :455-465
[10]   Water-level management as a tool for the restoration of shallow lakes in the Netherlands [J].
Coops, H ;
Hosper, SH .
LAKE AND RESERVOIR MANAGEMENT, 2002, 18 (04) :293-298