Inflow forecasting for real-time reservoir operation using artificial neural network

Taesoon Kim, Gian Choi, Jun Haeng Heo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Artificial neural network (ANN) is used for inflow forecasting of reservoir up to the next 12 hours. Numerical weather forecasting information (RDAPS), recorded rainfall data, water level of upstream dam and stream gauge site, and inflow of the current time are employed as input layer's training values, and target value is +3, +6, +9, and +12 hours later inflow to Hwacheon reservoir in South Korea. Comparison result between ANN with RDAPS and without RDAPS shows that RDAPS information is useful for forecasting inflow of reservoir.

Original languageEnglish
Title of host publicationProceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009
Subtitle of host publicationGreat Rivers
Pages4947-4955
Number of pages9
DOIs
Publication statusPublished - 2009 Oct 26
EventWorld Environmental and Water Resources Congress 2009: Great Rivers - Kansas City, MO, United States
Duration: 2009 May 172009 May 21

Publication series

NameProceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers
Volume342

Other

OtherWorld Environmental and Water Resources Congress 2009: Great Rivers
CountryUnited States
CityKansas City, MO
Period09/5/1709/5/21

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)

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    Kim, T., Choi, G., & Heo, J. H. (2009). Inflow forecasting for real-time reservoir operation using artificial neural network. In Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers (pp. 4947-4955). (Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers; Vol. 342). https://doi.org/10.1061/41036(342)499