Classification of Flood Levels using Random Forest and Support Vector Machine Algorithms

https://doi.org/10.22146/ijeis.97043

Larasati Syarafina Qamarani(1*), Mardhani Riasetiawan(2)

(1) University of Gadjah Mada
(2) 
(*) Corresponding Author

Abstract


Floods are one of the most common natural disasters in Indonesia. This study analyzes the impact of each flood event by examining factors such as duration, water level, and the number of affected individuals to identify flood characteristics based on severity. Climate variables such as temperature, humidity, rainfall, and wind speed were investigated as parameters characterizing flood occurrences. The primary objective of this research is to classify flood levels using Random Forest and Support Vector Machine (SVM) algorithms, and to evaluate the accuracy of these classifications using a Confusion Matrix. The outcomes are intended to inform decision-making processes during floods, thereby aiming to minimize associated losses. The research utilized historical flood data from the DKI Jakarta BPBD, accessed through the Satu Data Jakarta website, and climate data from the BMKG Geophysical Station, covering the period from 2013 to 2020. The Random Forest classification system demonstrated exceptional performance, achieving an accuracy of 99.21%. Similarly, the SVM classification system performed robustly, with an accuracy of 98.43%. Both models initially exhibited overfitting during the early stages of model development. However, this issue is diminished as the dataset size increases, thereby enhancing the models' generalization capabilities.

Keywords


Machine learning; Flood

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References

Kundzewicz, Z.W., Pińskwar, I. and Brakenridge, G.R., 2018. Changes in river flood hazard in Europe: a review. Hydrology research, 49(2), pp.294-302, 2017 [Online]. Available:https://iwaponline.com/hr/article/49/2/294/37824/Changes-in-river-flood-hazard-in-Europe-a-review. [Accessed: 19-June-2023] [2] Behera, J., 2020. Classifying Flood Severity Using Machine Learning. Doctoral dissertation, Dublin, National College of Ireland, Nov. 2021 [Online]. Available: https://www.researchgate.net/publication/356372808_A_machine_learning_approach_to_flood_severity_classification_and_alerting. [Accessed: 17-June-2023] [3] Sharma, P., Kar, B., Wang, J. and Bausch, D., “A Machine Learning approach to flood severity classification and alerting,” ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, pp. 42-47, Nov. 2021 [Online]. Available: https://www.semanticscholar.org/paper/A-Machine-Learning-Approach-to-Flood-Depth-and-1A-B-Tiampo-Woods/1b085fdc46e5cb386e4f961c024129c39d89e656. [Accessed: 16-June-2023] [4] Alipour, A., Ahmadalipour, A., Abbaszadeh, P. and Moradkhani, H., 2020. Leveraging Machine Learning for predicting flash flood damage in the Southeast US. Environmental Research Letters, 15(2), p.024011, Feb. 2020 [Online]. Available: https://iopscience.iop.org/article/10.1088/1748-9326/ab6edd. [Accessed: 20-June-2023] [5] Khan, T.A., Shahid, Z., Alam, M., Su'ud, M.M. and Kadir, K., 2019, December. Early flood risk assessment using machine learning: A Comparative Study of SVM, Q-SVM, KNN, and LDA. In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), pp. 1-7, 2019 [Online]. Available: https://ieeexplore.ieee.org/document/9024796. [Accessed: 16-June-2023] [6] Khalaf, M., Hussain, A.J., Al-Jumeily, D., Baker, T., Keight, R., Lisboa, P., Fergus, P. and Al Kafri, A.S., 2018, July. A data science methodology based on Machine Learning algorithms for flood severity prediction. IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, Jul. 2020 [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/11187. [Accessed: 17-June-2023] [7] Fitriyaningsih, I., Basani, Y. and Ginting, L.M., Machine Learning: Prosperity Of Rainfall, Water Discharge, And Flood With Web Application In Deli Serdang-Aplikasi Prediksi Curah Hujan, Debit Air, dan Kejadian Banjir Berbasis Web dengan Machine Learning di Deli Serdang. Jurnal Penelitian Komunikasi dan Opini Publik, 22(2), p.272740, Dec. 2018 [Online]. Available: https://www.neliti.com/publications/272740/machine-learning-prosperity-of-rainfall-water-discharge-and-flood-with-web-appli. [Accessed: 1-July-2023] [8] Grady, F., Tarigan, J.K., Wahidiyat, J.R. and Prasetyo, A., November. Classification of Flood Alert in Jakarta with Random Forest. 2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), pp. 1-6, 2022 [Online]. Available: https://ieeexplore.ieee.org/document/9971411. [Accessed: 22-June-2023 [9] Kim, D., Park, J., Han, H., Lee, H., Kim, H.S. and Kim, S., 2023. Application of AI-Based Models for Flood Water Level Forecasting and Flood Risk Classification. KSCE Journal of Civil Engineering, pp.1-12, May 2023 [Online]. Available: https://link.springer.com/article/10.1007/s12205-023-2175-5. [Accessed: 1-February-2024] [10] Sharma, T., Pal, A., Kaushik, A., Yadav, A. and Chitragupta, A., 2022, February. A Survey on Flood Prediction analysis based on ML Algorithm using Data Science Methodology. 2022 IEEE Delhi Section Conference (DELCON) pp. 1-8, 2022 [Online]. Available: https://ieeexplore.ieee.org/document/9753396. [Accessed: 16-Feb-2017]



DOI: https://doi.org/10.22146/ijeis.97043

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