The Water Level and Outflow Prediction Using the Artificial Neural Network (ANN) for the Management of the Reservoir Flooding
Hari Nugroho1, Suripin2, Iwan K. Hadihardaja3
1Hari Nugroho, Civil Engineering Department, University of Diponegoro, Semarang, Indonesia.
2Suripin, Civil Engineering Department, University of Diponegoro, Semarang, Indonesia
3Iwan K. Hadihardaja, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Bandung, Indonesia.
Manuscript received on 02 August 2019 | Revised Manuscript received on 09 August 2019 | Manuscript published on 30 August 2019 | PP: 4699-4706 | Volume-8 Issue-10, August 2019 | Retrieval Number: J99580881019/2019©BEIESP | DOI: 10.35940/ijitee.J9958.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The aim of operation reservoir during flood is to prevent overflow that endangers the dams. It is also to prevent flooding in the downstream of the dam, which leads to loss of life and property. This aim can be achieved with optimal reservoir management which is influenced by the reservoir’s condition during flooding such as: rain, reservoir storage, inflow, water level, and discharge of reservoir water released to the downstream. The successfully of the reservoir management depends on the accuracy of the estimated a). water level (due to the inflow of the reservoir) and b). outflow from the reservoir. One of the models which can be used to predict the water level and reservoir water released during flooding is the Artificial Neural Network (ANN). ANN can simulates flood events that are similar in fact to the previous occurence In this study a backpropagation ANN model was applied to the Wonogiri Reservoir in Central Java, Indonesia. The optimal ANN architecture produced in this study are the Input Pattern of 5-3-4 (which has a rain input recorded 1 – 5 hours earlier, a water level input recorded 1 – 3 hours earlier and a release input recorded 1 – 4 hours earlier). 27 pieces hidden layer, total epoch which is 200 and the learning rate of 0.01. The output is predicting the water level, the Outflow and Gate Opening of Reservoir. The current flood data was applied to the above model and it was concluded that the network can follow the flood management pattern adequately. In addition, the network is extra flexible with a lower flood discharge rate; and has the final elevation of the reservoir slightly lower than the normal operation.
Keywords: Artificial Neural Network, Optimal Model, Reservoir Operation During Flood
Scope of the Article: Artificial Neural Network