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.
|Number of pages||6|
|Journal||Proceedings of the International Association of Hydrological Sciences|
|Publication status||Published - 12 May 2016|
|Event||7th International Water Resources Management Conference of IAHS-ICWRS 2016 - Bochum, Germany|
Duration: 18 May 2016 → 20 May 2016
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)