FORECASTING THE FLOOD STAGE OF A RESERVOIR BASED ON THE CHANGES IN UPSTREAM RAINFALL PATTERN
Keywords:Reservoir, flood forecasting, artificial neural network, flood stage, rainfall pattern
Flood is among the major disasters in Malaysia. Flood occurs when the existing waterways are unable to support large amounts of water during heavy rain seasons. Reservoirs have been used as one of the flood mitigation approaches in the country. A reservoir can hold excessive water to ensure water flow to the downstream area is under the safe capacity of the waterway. However, due to the needs of the society, a reservoir also serves other purposes such as water supply and recreation. Therefore, reservoir water storage should be maintained to satisfy water usage, and at the same time, the water needs to be released to reserve space for incoming water. This conflict causes problems to reservoir operators when making the water release decision. In this paper, a forecasting model was proposed to forecast the flood stage of a reservoir based on the upstream rainfall pattern. This model could be used by reservoir operators in the early decision-making stage of releasing water before the reservoir reaches its maximum capacity. Simultaneously, the reservoir water level could be maintained for other uses. In this study, the experiments conducted proved that an Artificial Neural Network is capable of producing an acceptable performance in terms of its accuracy.
Chang, F.-J., & Chang, Y.-T. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 29(1), 1â€“10
Hadi, Y.H., Ku-Mahamud, K.R. & Ishak, W.H.W. (2019). Extreme Rainfall Forecasting Model Based on Descriptive Indices. Journal of Technology and Operations Management, 14 (1), 35â€“52
Hipni, A., El-shafie, A., Najah, A., Karim, O. A., Hussain, A., & Mukhlisin, M. (2013). Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS). Water Resources Management, 27(10), 3803â€“3823
Ishak, W.H.W., Ku-Mahmud, K.R. & Norwawi, N.M. (2011) Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision. In J.M. Zain et al. (Eds.): ICSECS 2011, Part I, Communications in Computer and Information Science (CCIS) 179, pp. 365-379
Ishak, W.H.W., Ku-Mahmud, K.R. & Norwawi, N.M. (2015) Modelling of Human Expert Decision Making in Reservoir Operation. Journal Teknologi, 77(22), 1-5.
Mokhtar, S.A., Ishak, W.H.W., & Norwawi, N.M. (2016) Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir. Journal of Advanced Management Science, 4(6), pp. 503-506.
Nash, Eamonn and Jonh Sutcliffe (1970). River Flow Forecasting through Conceptual Models Part iâ€”A Discussion of Principles. Journal of Hydrology 10(3), 282-290.
Nwobi-Okoye, C. C., & Igboanugo, A. C. (2013). Predicting Water Levels at Kainji Dam using Artificial Neural Networks. Nigeria Journal of Technology, 32(1), 129â€“136.
Parthasarathi, Pavithra and David Levinson (2010). Post-Construction Evaluation of Traffic Forecast Accuracy. Transport Policy 17(6), 428-443.
Piasecki, A., Jurasz, J., & Skowron, R. (2015). Application of artificial neural networks (ANN) in Lake DrwÄ™ckie water level modelling. Limnological Review, 15(1).
Pipitone, C., Maltese, A., Dardanelli, G., Brutto, M.L. & Loggia, G.L. (2018). Monitoring Water Surface and Level of a Reservoir Using Different Remote Sensing Approaches and Comparison with Dam Displacements Evaluated via GNSS. Remote Sens., 10, 71; doi:10.3390/rs10010071
Rani, S., & Parekh, F. (2014). Application of Artificial Neural Network (ANN) for Reservoir Water Level Forecasting. International Journal of Science and Research, 3(7), 1077â€“1082.
Valizadeh, N., & El-Shafie, A. (2013). Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS. Water Resources Management, 27(9), 3319â€“3331.
Valizadeh, N., El-Shafie, A., Mukhlisin, M., & El-Shafie, A. H. (2011). Daily water level forecasting using adaptive neuro-fuzzy interface system with different scenarios: Klang Gate, Malaysia. International Journal of the Physical Sciences, 6(32), 7379â€“7389
Zakaria, N.H., Ahmad, M.N., Noor, M.S.A.M. & Ahmad, M. (2018) Knowledge Integration among Flood Disaster Management Team: Lessons from The Kemaman District. Journal of Information and Communication Technology (JICT), 17(3), 393-408
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