Optimization of Early Warning System for Landslides Based on Rainfall Using Naive Bayes Classifier and Multiclass Support Vector Machine Algorithm in Takari Region
Abstract
This study explores non-structural disaster mitigation approaches employed by researchers, utilizing machine learning algorithms to analyze weather data and assess landslide vulnerability in the Takari Sub-district. Through field investigations and secondary data analysis, the research underscores the significance of rainfall intensity as a key factor in triggering landslides in the region. Additionally, soil types and slope gradients are identified as critical considerations in landslide vulnerability detection. Evaluation of a multiclass support vector algorithm for rainfall prediction reveals a notable accuracy rate of 57.97%, with predictions indicating instances of various rainfall intensities. Factors influencing these predictions include average temperature, humidity, wind speed, duration of sunshine, and wind direction. However, the study notes limitations in predictive accuracy due to the constrained availability of rainfall data. Consequently, the findings emphasize the need for preemptive measures, urging governmental authorities and local communities to prioritize structural disaster mitigation strategies to mitigate the heightened susceptibility to landslides in the Takari region
Downloads
References
Adnan, M.S.G. et al. (2023) ‘A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction’, Journal of Environmental Management, 326(PB), p. 116813. Available at: https://doi.org/ 10.1016/ j.jenvman.2022. 116813.
Arhin, S.A. and Gatiba, A. (2020) ‘Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers’, Transportation Safety and Environment, 2(2), pp. 120–132. Available at: https://doi.org/10.1093/tse/tdaa012.
Billah, M. et al. (2023) ‘Random forest classifications for landuse mapping to assess rapid flood damage using Sentinel-1 and Sentinel-2 data’, Remote Sensing Applications: Society and Environment, 30(March), p. 100947. Available at: https://doi.org/10.1016 /j.rsase.2023.100947.
Boxwala, A.A. et al. (2011) ‘Using statistical and machine learning to help institutions detect suspicious access to electronic health records’, Journal of the American Medical Informatics Association, 18(4), pp. 498–505. Available at: https://doi.org/10.1136/amiajnl-2011-000217.
Bucherie, A. et al. (2022) ‘A comparison of social vulnerability indices specific to flooding in Ecuador: principal component analysis (PCA) and expert knowledge’, International Journal of Disaster Risk Reduction, 73(February), p. 102897. Available at: https://doi.org/10.1016/j.ijdrr.2022.102897.
Bui, D.T. et al. (2018) ‘Landslide detection and susceptibility mapping by AIRSAR data using support vector machine and index of entropy models in Cameron Highlands, Malaysia’, Remote Sensing, 10(10). Available at: https://doi.org/10.3390/rs10101527.
Dangeti, P. (2013) Statistics for Machine Learning Build, Journal of Chemical Information and Modeling.
Heydari, M. et al. (2020) ‘Application of holt-winters time series models for predicting climatic parameters (Case study: Robat Garah-Bil station, Iran)’, Polish Journal of Environmental Studies, 29(1), pp. 617–627. Available at: https://doi.org/10.15244/pjoes/100496.
Huang, J., Lu, J. and Ling, C.X. (2003) ‘Comparing naive bayes, decision trees, and SVM with AUC and accuracy’, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 553–556. Available at: https://doi.org/10.1109/icdm.2003.1250975.
Jennifer, J.J. (2022) ‘Feature elimination and comparison of machine learning algorithms in landslide susceptibility mapping’, Environmental Earth Sciences, 81(20), pp. 1–23. Available at: https://doi.org/10.1007/s12665-022-10620-5.
Kao, I.F. et al. (2021) ‘Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts’, Journal of Hydrology, 598(October 2020), p. 126371. Available at: https://doi.org/10.1016/j .jhydrol.2021.126371.
Leavitt, S. V. et al. (2021) ‘Estimating the relative probability of direct transmission between infectious disease patients’, International Journal of Epidemiology, 49(3), pp. 764–775. Available at: https://doi.org/10.1093/ IJE/DYAA031.
Findawati, Y., Astutik, I. I., Fitroni, A. S., Indrawati, I., & Yuniasih, N. (2019). Comparative analysis of Naïve Bayes, K Nearest Neighbor and C. 45 method in weather forecast. Journal of Physics: Conference Series, 1402(6), 066046. https://doi.org/10.1088/1742-6596/1402/6/066046
Liu, J. et al. (2021) ‘Assessment of Flood Susceptibility Using Support Vector Machine in the Belt and Road Region’, Natural Hazards and Earth System Sciences Discussions, (May), pp. 1–37.
Choi, Y., Kang, B., & Kim, D. (2024). Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea. Aerosol and Air Quality Research, 24, 230222.
Al Mehedi, M.A. et al. (2023) ‘Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network’, Journal of Hydrology, 625(PA), p. 130076. Available at: https://doi.org/10.1016/j.jhydrol.2023.130076.
Oikonomou, E.K. and Khera, R. (2023) ‘Machine learning in precision diabetes care and cardiovascular risk prediction’, Cardiovascular Diabetology, 22(1), pp. 1–16. Available at: https://doi.org/10.1186/s12933-023-01985-3.
Pradeep, K.R. and Naveen, N.C. (2018) ‘Lung Cancer Survivability Prediction based on Performance Using Classification Techniques of Support Vector Machines, C4.5 and Naive Bayes Algorithms for Healthcare Analytics’, Procedia Computer Science, 132, pp. 412–420. Available at: https://doi.org/10.1016/j.procs.2018.05.162.
Pradhan, B. (2013) ‘Computers & Geosciences A comparative study on the predictive ability of the decision tree , support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS’, Computers and Geosciences, 51, pp. 350–365. Available at: https://doi.org/10.1016/j.cageo.2012.08.023.
Rosa, M.L., Sobreira, F.G. and Barella, C.F. (2021) ‘Landslide susceptibility mapping using the statistical method of information value: A study case in Ribeirão dos Macacos basin, Minas Gerais, Brazil’, Anais da Academia Brasileira de Ciencias, 93(1), pp. 1–19. Available at: https://doi.org/10.1590/0001-3765202120180897.
Rudra, R.R. and Sarkar, S.K. (2023) ‘Artificial neural network for flood susceptibility mapping in Bangladesh’, Heliyon, 9(6), p. e16459. Available at: https://doi.org/10. 1016/j.heliyon.2023.e16459.
Saha, T.K. and Pal, S. (2019) ‘Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches’, Ecological Indicators, 98(November 2018), pp. 251–265. Available at: https://doi.org/10.1016/j.ecolind.2018.11.009.
Shahzad, N., Ding, X. and Abbas, S. (2022) ‘A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan’, Applied Sciences (Switzerland), 12(5). Available at: https://doi.org/10.3390/app12052280.
Shinohara, E.J. (2012) ‘Assessment of SVM classification process for landslides identification Assessment of SVM classification process for landslides identification’, (July 2014).
Stephen, M. (2014) Machine Learning An Algorithmic Perspective Second Edition. Available at: https://b-ok.cc/book/2543746/ef80cb.
Tang, X. et al. (2020) ‘Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods’, Catena, 190(March), p. 104536. Available at: https://doi.org/10.1016/j.catena.2020.104536.
Xu, H. et al. (2018) ‘Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China’, Journal of Hydrology, 563(March), pp. 975–986. Available at: https://doi.org/10.1016/j.jhydrol.2018.06.060.
Zaki, M.J. and Meira, M.J. (2013) Data Mining and Analysis: Fundamental Concepts and Algorithms. Available at: https://books.google.com.tr/books?id=Gh9 GAwAAQBAJ&lpg=PR9&dq=Data Mining and Analysis: FoundationsandAlgorithms&hl=t r&pg=PR9#v=onepage &q=Data Mining and Analysis: Foundations and Algorithms&f=false.
Copyright (c) 2024 Timor-Leste Journal of Engineering and Science
This work is licensed under a Creative Commons Attribution 4.0 International License.