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Ainul Najwa Azahari College of Computing, Informatics and Mathematics, UiTM Cawangan Terengganu, Kampus Kuala Terengganu, Malaysia Norlina Mohd Sabri Research and Industrial Linkages Unit, UiTM Cawangan Terengganu, Kampus Kuala Terengganu, Malaysia
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| Abstract | |
| Floods are one of nature's deadliest catastrophes, causing permanent and catastrophic damage on the socioeconomic system, agriculture and human life. The problems arise when floods could cause a lot of economic damage such as damage to buildings, agriculture and others. Flood damage estimation is a subject of study that has not received much attention. The objective of this research is to explore the Random Forest algorithm in the flood damage cost prediction. The damages specified by the Malaysia’s Department of Irrigation (JPS) are structures such as culverts, MTB bridges, riverbank ruins, concrete main channels, farm roads, hydrological stations, agricultural and water drainage, JPS pump houses and tyres in Terengganu. Terengganu is one of the states in Malaysia which has to endure floods during the monsoon season by the end of the year. The methods employed in this research include data collection, data pre-processing, backend engine coding and user interface design. This project was implemented using the Python programming language. The data were collected from the annual flood report provided by the JPS Negeri Terengganu. The research used the rainfall and streamflow data from the year 2012 to 2022 as attributes to forecast the cost of the JPS structures damages in Terengganu. The prediction results showed that the best model achieved the accuracy of 91.47% with a Mean Squared Logarithmic Error (MSLE) of 0.48 and Coefficient of Determination (R2) of 0.92. In the performance evaluation, the model with 80:20 training and testing data ratio produced the best result in predicting the flood damage cost. The potential enhancements to this research involve extending the scope to encompass all Malaysian states, incorporating diverse flooded structures and adding more input variables for a more improved and more reliable flood prediction system. |
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| Keyword: Prediction, Machine Learning, Flood Damage, Damage Cost, Random Forest | |
| References: | |
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[1] D. M. García, J. R. G. Torga, M. D. Pinheiro and J. Moyano, "Simplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area," Sustainable Cities and Society, vol. 88, pp. 104251, 2022. Available: http://dx.doi.org/10.1016/j.scs.2022.104251 [2] Snehil and R. Goel, "Flood Damage Analysis Using Machine Learning Techniques," Procedia Computer Science, vol. 173, pp. 78 - 85, 2020. Available: http://dx.doi.org/10.1016/j.procs.2020.06.011 [3] M. B. Malgwi, M. Schlogl and M. Keiler, "Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions," International Journal of Disaster Risk Reduction, vol. 57, pp. 102148, 2021. [4] E. L. Collins, G. M. Sanchez, A. Terando, C. C. Stillwell, H. Mitasova, A. Sebastian and R. K. Meentemeyer, "Predicting flood damage probability across the conterminous United States," Environmental Research Letters, vol. 17, no. 3, pp. 034006, 2022. Available: http://dx.doi.org/10.1016/j.ijdrr.2021.102148 [5] Z. Jiang, S. Yang, Z. Liu, Y. Xu, Y. Xiong, S. Qi, Q. Pang, J. Xu, F. Liu and T. Xu, "Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin," Environmental Modelling & Software, vol. 155, pp. 105436, 2022. [6] A. Alipour, A. Ahmadalipour, P. Abbaszadeh and H. Moradkhani, "Leveraging machine learning for predicting flash flood damage in the Southeast US," Environmental Research Letters, vol. 15, no. 2, pp. 024011, 2020. Available: http://dx.doi.org/10.1088/1748-9326/ab6edd [7] S. Dhanka and S. Maini, "Random Forest for Heart Disease Detection: A Classification Approach," 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES), pp. 1 - 3, 2021. [8] M. S. Kumar, V. Soundarya, S. Kavitha, E. S. Keerthika and E. Aswini, "Credit Card Fraud Detection Using Random Forest Algorithm," 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), pp. 149 - 153, 2019. [9] S. Fan and M. Fu, "Music Genre Recommendation Based on MLP & Random Forest," 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE), pp. 331 - 334, 2022. [10] A. Zermane, M. Z. M. Tohir, H. Zermane, M. R. Baharudin and H. M. Yusoff, "Predicting fatal fall from heights accidents using random forest classification machine learning model," Safety Science, vol. 159, 2022. [11] A. T. Prihatno, H. Nurcahyanto and Y. M. Jang, "Predictive Maintenance of Relative Humidity Using Random Forest Method," 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 497 - 499, 2021. [12] Y. Jiang, J. Huang, W. Luo, K. Chen, W. Yu, W. Zhang, C. Huang, J. Yang and Y. Huang, "Prediction for odor gas generation from domestic waste based on machine learning," Waste Management, vol. 156, pp. 264 - 271, 2022. [13] V. K. Gupta, A. Gupta, D. Kumar and A. Sardana, "Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model," Big Data Mining and Analytics, vol. 4, no. 2, pp. 116 - 123, 2021. Available: http://dx.doi.org/10.26599/BDMA.2020.9020016 |
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Flood damage cost prediction using random forest
