| Summary: | 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.
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