A study on real-time forecasting of reservoir inflow based on Artificial Neural Network

Chang Sam Jeong, Won Jun Koh, Jun-Haeng Heo

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

1 Citation (Scopus)

Abstract

For the most effective operation of multi-purpose reservoir at flood period, the forecasting of inflow must be preceded and a rainfall-runoff modeling is necessary for the forecasting of inflow. However, the rainfall-runoff process is nonlinear and complex so many errors can be occurred by uncertain parameter estimation in modeling procedure. In this study, a neural network theory was adopted for modeling rainfall-runoff process, and a real-time inflow forecast system was developed. The models developed in this study were based on the back-propagation algorithm and Cascade-Correlation algorithm for learning. We applied these models to Soyangang River basin, so we could get forecasted inflow values., 1 hour, 3 hour and 6 hour preceding inflows. In case of the back-propagation algorithm, many trials are required to find out the optimum structure, but Cascade-Correlation algorithm can make the optimum neural network structure automatically at a time. We applied this model to the August '95 flood event at Soyangang River basin by using Cascade-Correlation algorithm and back-propagation algorithm. In order to improve the accuracy of the flood forecasting, the filtering technique has been used at the neural network model. As a result, Cascade-Correlation filtering model shows better forecasting capability.

Original languageEnglish
Title of host publicationWatershed Management and Operations Management 2000
Volume105
DOIs
Publication statusPublished - 2004 Dec 1
EventWatershed Management and Operations Management 2000 - Fort Collins, CO, United States
Duration: 2000 Jun 202000 Jun 24

Other

OtherWatershed Management and Operations Management 2000
CountryUnited States
CityFort Collins, CO
Period00/6/2000/6/24

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All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Cite this

Jeong, C. S., Koh, W. J., & Heo, J-H. (2004). A study on real-time forecasting of reservoir inflow based on Artificial Neural Network. In Watershed Management and Operations Management 2000 (Vol. 105) https://doi.org/10.1061/40499(2000)82