1Department of Hydrology and Meteorology, Hanoi University of Natural Resources and Environment


In Vietnam, modern and small hydropower reservoirs play an important role in socio-economic development. However, the effective operation of such reservoirs based on the argument of the release function and expected future inflow is one of the most important variables that the operators will reply on to control such release. In other words, to attenuate floods, the operators have to release water in advance, so to create an empty volume (flood volume) in the reservoir, into which the excess in flow can be accommodated during the flood events. Therefore, the predicted periods should be as long as possible to create a sufficient large flood volume in the reservoir, while releasing a flow that is not so high to mitigate the impacts on downstream. Nevertheless, predicting the future inflow is still a big challenge for the local hydrologists due to the lack of information and technology. This paper proposes a method to predict the inflow of Pleikrong hydropower reservoir which located in downstream of Poko river, a second tributary of SeSan River and the observation data is insufficient and incorrect. This method uses MIKE NAM to construct the inflow then the Artificial Neuron Network to predict the inflow based on the availability of data. The result is surprising when R2for 6 hourly forecasted inflow is about 0.97 and for 12 hourly forecasted is about 0.79 which correspond to the catchment concentration time of 9 hours. The results of this study will hopefully be an example to apply on many case studies in Viet Nam and other ungauged stations system. 


Cite this paper

Truong Van Anh, Duong Tien Dat (2018), Artifical Neuron Network for Flood Forecasting as Inflow of Pleikrong Reservoir in Poko River. Vietnam Journal of Hydrometeorology, 01, 54-63.


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