Inflow forecasting using artificial neural networks for reservoir operation

Chuthamat Chiamsathit, Adebayo J. Adeloye, Soundharajan Bankaru-Swamy

Research output: Contribution to journalConference article

11 Citations (Scopus)
79 Downloads (Pure)

Abstract

In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

Original languageEnglish
Pages (from-to)209-214
Number of pages6
JournalProceedings of the International Association of Hydrological Sciences
Volume373
DOIs
Publication statusPublished - 12 May 2016
Event7th International Water Resources Management Conference of IAHS-ICWRS 2016 - Bochum, Germany
Duration: 18 May 201620 May 2016

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'Inflow forecasting using artificial neural networks for reservoir operation'. Together they form a unique fingerprint.

Cite this