Reservoir water level forecasting model using neural network

• Main Authors: Wan Ishak, Wan Hussain, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita
• Format: Article
• Language: English
• Published: Research India Publications 2010
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/3994/1/ijcirvol6no4.pdf; http://repo.uum.edu.my/3994/; http://www.ripublication.corn/ijcir.htm

Reservoir is one of the structural defense mechanism for flood. During heavy rainfall, reservoir hold excessive amount of water to reduce flood risk at downstream area. During less rainfall, reservoir maintains the water supply for major uses such as domestic and commercial usage. In both situations, the water release decision is very critical. The decision is typically influence by the reservoir storage capacity that is the reservoir water level. Early decision regarding the water release can be made if the future water level can be forecasted. In this paper, the potential of neural network model for forecasting the reservoir water level is experimented. The time delay of upstream flow to increase the water level is also experimented. Sliding windows have been used to segment the data into a various range. The findings show that 8 days for delay has significantly affected the reservoir water level. The best neural network model obtain from the experiment is 24-15-3.