Flood Risk Prediction for a Hydropower System using Artificial Neural Network
Nurul Najwa Anuar1, M. Reyasudin Basir Khan2, Jagadeesh Pasupuleti3, Aizat Faiz Ramli4
1Nurul Najwa Anuar, Universiti Kuala Lumpur, British Malaysian Institute, 53100 Gombak Selangor, Malaysia.
2M. Reyasudin Basir Khan, Universiti Kuala Lumpur, British Malaysian Institute, 53100 Gombak Selangor, Malaysia.
3Jagadeesh Pasupuleti, College of Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia.
4Aizat Faiz Ramli, Universiti Kuala Lumpur, British Malaysian Institute, 53100 Gombak Selangor, Malaysia.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 6177-6181 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5134118419/2019©BEIESP | DOI: 10.35940/ijrte.D5134.118419
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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: Hydropower scheme would experience issue relating to high flooding especially at low lying area due to extreme raining season. To mitigate the potential risk of flooding and improve the hydroelectric regulation, a flow prediction is needed to estimate the discharge of water flow at hydroelectric reservoirs. Artificial Neural Network (ANN) model were used in this research to forecast the water discharge of hydroelectric station. The discharge flow predictions were made based on fore bay elevation, inflow and the discharge of water flow. Elman Neural Network architecture was selected as ANN method and its performance was evaluated by considering the number of hidden nodes and training methods. ANN model performance were assessed using performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE) and Sum Square Error (SSE). The result indicate that ANN model showed the best applicability for discharge prediction with small performance metric.
Keywords: Hydropower, Artificial Neural Network, Elman Neural Network, Flow prediction.
Scope of the Article: Neural Information Processing.