SISTEM PERINGATAN DINI BANJIR BERBASIS SUPPORT VECTOR MACHINE YANG DIOPTIMALKAN DENGAN PARTICLE SWARM OPTIMIZATION
Abstract
Flooding is one of the most frequently occurring hydrometeorological disasters and causes substantial impacts on human safety, infrastructure damage, and economic losses. This condition necessitates the development of flood early warning systems that are accurate, reliable, and capable of providing timely information. This study aims to develop a flood early warning system based on Support Vector Machine optimized using Particle Swarm Optimization to improve the prediction performance of flood events. The methodology involves constructing a Support Vector Machine model with hyperparameter optimization carried out through Particle Swarm Optimization. The performance of the proposed model is compared with a Support Vector Machine without optimization and a Support Vector Machine optimized using the GridSearch method. Model performance is evaluated using several metrics, including accuracy, precision, recall, F1-score, and specificity, and is further analyzed through confusion matrices, learning curves, Receiver Operating Characteristic curves, and optimization convergence. The results demonstrate that the Support Vector Machine–Particle Swarm Optimization model achieves the best performance, with an accuracy of approximately 96% and an optimal F1-score of 0.9777. The model successfully detects all flood events without any false negatives and produces only one false positive, indicating very high sensitivity and reliability. Learning curve analysis shows stable generalization capability, while the optimization process exhibits rapid and consistent convergence in the early iterations. Compared with the benchmark models, this approach also provides better classification balance and higher computational efficiency
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References
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[2] UNDRR, "Human Cost of Disasters: An Overview of the Last 20 Years (2000-2019)," United Nations Office for Disaster Risk Reduction, Geneva, 2020.
[3] S. Hirabayashi et al., "Global flood risk under climate change," Nature Climate Change, vol. 3, no. 9, pp. 816-821, 2013.
[4] E. Alfieri, P. Salamon, A. Bianchi, J. Neal, and P. Bates, "Advances in pan-European flood hazard mapping," Hydrological Processes, vol. 28, no. 13, pp. 4067-4077, 2014.
[5] G. Nearing et al., "What role does hydrological science play in the age of machine learning?," Water Resources Research, vol. 57, no. 1, e2020WR028091, 2021.
[6] F. Kratzert, D. Klotz, M. Herrnegger, A. K. Sampson, S. Hochreiter, and G. S. Nearing, "Toward improved predictions in ungauged basins: Exploiting the power of machine learning," Water Resources Research, vol. 55, no. 12, pp. 11344-11354, 2019.
[7] A. Mosavi, P. Ozturk, and K. W. Chau, "Flood prediction using machine learning models: Literature review," Water, vol. 10, no. 11, p. 1536, 2018.
[8] C. Sit, B. Z. Demiray, Z. Xiang, G. J. Ewing, Y. Sermet, and I. Demir, "A comprehensive review of deep learning applications in hydrology and water resources," Water Science and Technology, vol. 82, no. 12, pp. 2635-2670, 2020.
[9] A. Mosavi, P. Ozturk, and K. W. Chau, "Flood prediction using machine learning models: Literature review," Water, vol. 10, no. 11, p. 1536, 2018.
[10] R. Costache and D. T. Bui, "Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles," Science of the Total Environment, vol. 712, p. 136492, 2020.
[11] D. T. Bui, B. Pradhan, O. Lofman, I. Revhaug, and O. B. Dick, "Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models," Catena, vol. 96, pp. 28-40, 2012.
[12] V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd ed. New York, NY, USA: Springer, 2000.
[13] C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[14] M. S. Tehrany, B. Pradhan, and M. N. Jebur, "Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS," Journal of Hydrology, vol. 512, pp. 332-343, 2014.
[15] C. W. Hsu, C. C. Chang, and C. J. Lin, "A practical guide to support vector classification," Department of Computer Science, National Taiwan University, Taipei, Tech. Rep., 2003.
[16] M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, NY, USA: Springer, 2013.
[17] J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," Journal of Machine Learning Research, vol. 13, no. 1, pp. 281-305, 2012.
[18] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Networks, vol. 4, 1995, pp. 1942-1948.
[19] R. Poli, J. Kennedy, and T. Blackwell, "Particle swarm optimization: An overview," Swarm Intelligence, vol. 1, no. 1, pp. 33-57, 2007.
[20] Y. Shi and R. C. Eberhart, "A modified particle swarm optimizer," in Proc. IEEE Int. Conf. Evolutionary Computation, 1998, pp. 69-73.
[21] S. S. Keerthi and C. J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural Computation, vol. 15, no. 7, pp. 1667-1689, 2003.
[22] M. S. Tehrany, B. Pradhan, S. Mansor, and N. Ahmad, "Flood susceptibility assessment using GIS-based support vector machine model with different kernel types," Catena, vol. 125, pp. 91-101, 2015.
[23] D. T. Bui, K. Khosravi, S. Lee, H. Shahabi, B. Pradhan, and J. J. Clague, "Flood spatial modeling in northern Iran using remote sensing and GIS: a comparison between evidential belief functions and its ensemble with a multivariate logistic regression model," Remote Sensing, vol. 11, no. 13, p. 1589, 2019.
[24] H. Hong, P. Tsangaratos, I. Ilia, J. Liu, A. X. Zhu, and W. Chen, "Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China," Science of the Total Environment, vol. 625, pp. 575-588, 2018.
[25] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
[26] H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, 2009.
[27] A. Fernández, S. Garcia, F. Herrera, and N. V. Chawla, "SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary," Journal of Artificial Intelligence Research, vol. 61, pp. 863-905, 2018.
[28] M. Buda, A. Maki, and M. A. Mazurowski, "A systematic study of the class imbalance problem in convolutional neural networks," Neural Networks, vol. 106, pp. 249-259, 2018.
[29] India Meteorological Department, "Rainfall Statistics of India," Ministry of Earth Sciences, Government of India, New Delhi, 2019.
[30] V. Mishra, S. Aaadhar, H. Shah, R. Kumar, D. R. Pattanaik, and A. D. Tiwari, "The Kerala flood of 2018: combined impact of extreme rainfall and reservoir storage," Hydrology and Earth System Sciences Discussions, pp. 1-13, 2018.
[31] India Meteorological Department, "Rainfall Statistics of India," Ministry of Earth Sciences, Government of India, New Delhi, 2019.
[32] V. Mishra et al., "The Kerala flood of 2018: combined impact of extreme rainfall and reservoir storage," Hydrology and Earth System Sciences, vol. 22, no. 12, pp. 6633-6649, 2018.
[33] S. García, J. Luengo, and F. Herrera, Data Preprocessing in Data Mining. Cham, Switzerland: Springer, 2015.
[34] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proc. 14th Int. Joint Conf. Artificial Intelligence, vol. 2, 1995, pp. 1137-1143.
[35] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2012.
[36] S. Kaufman et al., "Leakage in data mining: Formulation, detection, and avoidance," ACM Transactions on Knowledge Discovery from Data, vol. 6, no. 4, pp. 1-21, 2012.
[37] N. V. Chawla et al., "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
[38] A. Fernández et al., "SMOTE for learning from imbalanced data: progress and challenges," Journal of Artificial Intelligence Research, vol. 61, pp. 863-905, 2018.
[39] H. Han, W. Y. Wang, and B. H. Mao, "Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning," in Proc. Int. Conf. Intelligent Computing, 2005, pp. 878-887.
[40] V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd ed. New York, NY, USA: Springer, 2000.
[41] C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[42] B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines. Cambridge, MA, USA: MIT Press, 2002.
[43] C. W. Hsu, C. C. Chang, and C. J. Lin, "A practical guide to support vector classification," National Taiwan University, Tech. Rep., 2003.
[44] S. S. Keerthi and C. J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural Computation, vol. 15, no. 7, pp. 1667-1689, 2003.
[45] M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, NY, USA: Springer, 2013.
[46] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Networks, vol. 4, 1995, pp. 1942-1948.
[47] Y. Shi and R. C. Eberhart, "A modified particle swarm optimizer," in Proc. IEEE Int. Conf. Evolutionary Computation, 1998, pp. 69-73.
[48] R. Poli, J. Kennedy, and T. Blackwell, "Particle swarm optimization: An overview," Swarm Intelligence, vol. 1, no. 1, pp. 33-57, 2007.
[49] D. M. W. Powers, "Evaluation: from precision, recall and F-measure to ROC," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63, 2011.
[50] F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
[51] M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations," Int. Journal of Data Mining & Knowledge Management Process, vol. 5, no. 2, pp. 1-11, 2015.
[52] D. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy," BMC Genomics, vol. 21, no. 1, p. 6, 2020.
[53] T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, 2006.
[54] P. Refaeilzadeh, L. Tang, and H. Liu, "Cross-validation," in Encyclopedia of Database Systems, L. Liu and M. T. Özsu, Eds. Boston, MA, USA: Springer, 2009, pp. 532-538.
[55] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, 2nd ed. New York, NY, USA: Springer, 2021.
Published
2026-02-07
How to Cite
RIZAL H, Muhammad; M SABIR, Fitriana; MURSALIM, Mursalim.
SISTEM PERINGATAN DINI BANJIR BERBASIS SUPPORT VECTOR MACHINE YANG DIOPTIMALKAN DENGAN PARTICLE SWARM OPTIMIZATION.
Journal of Information System, Applied, Management, Accounting and Research, [S.l.], v. 10, n. 1, p. 155-167, feb. 2026.
ISSN 2598-8719.
Available at: <https://journal.stmikjayakarta.ac.id/index.php/jisamar/article/view/2260>. Date accessed: 07 feb. 2026.
doi: https://doi.org/10.52362/jisamar.v10i1.2260.
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
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