DOI: https://doi.org/10.14710/geoplanning.11.1.43-56

Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province

Camelia Batun Abrar  -  Jurusan Fisika, UNIB, Indonesia
*Ashar Muda Lubis  -  Jurusan Fisika, FMIPA, Universitas Bengkulu, Indonesia
Darmawan Ikhlas Fadli  -  Jurusan Fisika, UNIB, Indonesia
Arya J Akbar  -  , Indonesia
Rida Samdara  -  Jurusan Fisika, UNIB, Indonesia

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Abstract

Landslides occur when masses of rock, debris or soil move due to various factors and processes that cause land movement. The Taba Penanjung-Kepahiang route is one of the areas in Bengkulu Province that is highly prone to landslides. This causeway is the only fastest land route connecting the Bengkulu-Kepahiang area. In recent years, the road area has often been cut off due to landslides and fallen trees, which have caused road access to be cut off and obstructed and claimed lives. This study uses a Machine Learning (ML) and GIS approach with Variable Frequency Ratio using 16 independent factors obtained from the spatial database and DEM, which correlate with landslide events. This research aims to gain an in-depth understanding of the factors that cause landslides. In addition, the research focus is the development of a Disaster Mitigation Model to design and implement effective strategies to reduce the risk and impact of landslide disasters through in-depth analysis The dependent factor is the location of the landslide from the historical landslide area for the last five years, with a distribution of 70/30%. Furthermore, frequency ratio is used to analyze the correlation between conditioning factors and historical landslides. Then, the independent and dependent factors were normalized to create a landslide susceptibility map. Frequency Ratio (FR) indicates the likelihood of an event occurring, with drainage density (FR= 0.69), shear wave velocity (Vs30) (FR= 0.66), slope (FR= 0.60), and rainfall (FR= 0.55).  The output of the processed data is in the table below.

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Keywords: Landslide, Machine Learning, Frequency Ratio
Funding: Department of Physics, University of bengkulu

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Section: Original Research
Language : EN
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  1. Alcántara-Ayala, I., & Sassa, K. (2023). Landslide risk management: from hazard to disaster risk reduction. Landslides, 20(10), 2031-2037.[Crossref]

    " target="_blank">10.1007/s10346-023-02140-5">[Crossref]

  2. Aldiansyah, S., & Wardani, F. (2024). Assessment of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia. Water Practice & Technology, 19(1), 52-81. https://doi.org/10.2166/wpt.2024.002">[Crossref]

  3. Arabameri, A., Pourghasemi, H. R., & Yamani, M. (2017). Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environmental earth sciences, 76, 1-20. [Crossref]

    " target="_blank">10.1007/s12665-017-7177-5">[Crossref]

  4. Bednarik, M., Magulová, B., Matys, M., & Marschalko, M. (2010). Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study. Physics and Chemistry of the Earth, Parts A/B/C, 35(3-5), 162-171. https://doi.org/10.1016/j.pce.2009.12.002">[Crossref]

  5. Çevik, E., & Topal, T. (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental geology, 44, 949-962. https://doi.org/10.1007/s00254-003-0838-6">[Crossref]

  6. Chen, X., & Chen, W. (2021). GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena, 196, 104833. https://doi.org/10.1016/j.catena.2020.104833">[Crossref]

  7. Chopra, S., & Marfurt, K. (2007). Seismic curvature attributes for mapping faults/fractures, and other stratigraphic. CSEG Recorder, (November), 38–42.

  8. Ciurleo, M., Calvello, M., & Cascini, L. (2016). Susceptibility zoning of shallow landslides in fine grained soils by statistical methods. Catena, 139, 250-264. https://doi.org/10.1016/j.catena.2015.12.017">[Crossref]

  9. Cruden, D., & VanDine, D. F. (2013). Classification, description, causes and indirect effects-Canadian technical guidelines and best practices related to landslides: a national initiative for loss reduction. Geological survey of Canada, Open file, 7359, 22. https://doi.org/10.4095/292505">[Crossref]

  10. Dahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., & Nishino, K. (2008). GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54, 311-324. [Crossref]

    " target="_blank">10.1007/s00254-007-0818-3">[Crossref]

  11. Dai, F. C., & Lee, C. F. (2001). Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Canadian Geotechnical Journal, 38(5), 911-923. https://doi.org/10.1139/t01-021">[Crossref]

  12. Darminto, M. R., Widodo, A., Alfatinah, A., & Chu, H. J. (2021). High-resolution landslide susceptibility map generation using machine learning (Case Study in Pacitan, Indonesia). International Journal on Advanced Science, Engineering and Information Technology, 11(1), 369-379. [Crossref]

    " target="_blank">10.18517/ijaseit.11.1.11679">[Crossref]

  13. Dou, J., Yunus, A. P., Bui, D. T., Merghadi, A., Sahana, M., Zhu, Z., ... & Pham, B. T. (2020). Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides, 17, 641-658.https://doi.org/10.1007/s10346-019-01286-5"> [Crossref]

  14. Fadilah, N., Arsyad, U., & Soma, A. S. (2019). Analisis tingkat kerawanan tanah longsor menggunakan metode frekuensi rasio di Daerah Aliran Sungai Bialo. Perennial, 15(1), 42-50. https://doi.org/10.24259/perennial.v15i1.6317">[Crossref]

  15. Fan, H., Lu, Y., Hu, Y., Fang, J., Lv, C., Xu, C., ... & Liu, Y. (2022). A landslide susceptibility evaluation of highway disasters based on the frequency ratio coupling model. Sustainability, 14(13), 7740. https://doi.org/10.3390/su14137740">[Crossref]

  16. Froude, M. J., & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18(8), 2161-2181. https://doi.org/10.5194/nhess-18-2161-2018">[Crossref]

  17. Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196. https://doi.org/10.3390/rs11020196">[Crossref]

  18. Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1-2), 42-66. https://doi.org/10.1016/j.earscirev.2012.02.001">[Crossref]

  19. Hadi, A. I., Farid, M., Harlianto, B., & Sari, J. I. (2021). Landslide Potential Investigation for Disaster Risk Reduction in Central Bengkulu Regency, Bengkulu Province, Indonesia. Indonesian Journal on Geoscience, 8(3). [Crossref]

    " target="_blank">10.17014/ijog.8.3.313-328">[Crossref]

  20. Hadi, A. I., & Siswanto, B. K. (2016). Landslide potential analysis using microtremor and slope data on Bengkulu‑Kepahiang Main Road at Km 31–60. IOSR Journal of Applied Geology and Geophysic, 4, 9-14.

  21. Hadi, A. I., Brotopuspito, K. S., Pramumijoyo, S., & Hardiyatmo, H. C. (2018). Regional landslide potential mapping in earthquake-prone areas of Kepahiang Regency, Bengkulu Province, Indonesia. Geosciences, 8(6), 219. https://doi.org/10.3390/geosciences8060219">[Crossref]

  22. He, S., Pan, P., Dai, L., Wang, H., & Liu, J. (2012). Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology, 171, 30-41.https://doi.org/10.1016/j.geomorph.2012.04.024">[Crossref]

  23. Huang, F., Cao, Z., Guo, J., Jiang, S. H., Li, S., & Guo, Z. (2020). Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena, 191, 104580. https://doi.org/10.1016/j.catena.2020.104580">[Crossref]

  24. Irawan, L. Y., Bachri, S., Panoto, D., Pradana, I. H., Faizal, R., Devy, M. M. R., & Prasetyo, W. E. (2021). The Use of Machine Learning for Accessing Landslide Susceptibility Class: Study Case of Kecamatan Pacet, Kabupaten Mojokerto. In IOP Conference Series: Earth and Environmental Science (Vol. 884, No. 1, p. 012006). IOP Publishing. https://doi.org/10.1088/1755-1315/884/1/012006">[Crossref]

  25. Kavzoglu, T., Colkesen, I., & Sahin, E. K. (2019). Machine learning techniques in landslide susceptibility mapping: a survey and a case study. Landslides: Theory, practice and modelling, 283-301. [Crossref]

    " target="_blank">10.1007/978-3-319-77377-3_13">[Crossref]

  26. Koutsoyiannis, D. (2004). Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation of long rainfall records/Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: II. Recherche empirique sur de longues séries de précipitations. Hydrological Sciences Journal, 49(4). https://doi.org/10.1623/hysj.49.4.591.54424">[Crossref]

  27. Lee, J. H., Sameen, M. I., Pradhan, B., & Park, H. J. (2018). Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology, 303, 284-298. https://doi.org/10.1016/j.geomorph.2017.12.007">[Crossref]

  28. Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental geology, 40, 1095-1113. [Crossref]

    " target="_blank">10.1007/s002540100310">[Crossref]

  29. Lisle, R. J. (1994). 高斯曲率与高应变被引400, 12(12), 1811–1819.

  30. Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234. https://doi.org/10.1016/j.enggeo.2011.09.006">[Crossref]

  31. Mohammady, M., Pourghasemi, H. R., & Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61, 221-236. https://doi.org/10.1016/j.jseaes.2012.10.005">[Crossref]

  32. Moore, I. D., & Grayson, R. B. (1991). Terrain‐based catchment partitioning and runoff prediction using vector elevation data. Water Resources Research, 27(6), 1177-1191. https://doi.org/10.1029/91WR00090">[Crossref]

  33. Nagarajan, R., Roy, A., Vinod Kumar, R., Mukherjee, A., & Khire, M. V. (2000). Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bulletin of Engineering Geology and the Environment, 58, 275-287. [Crossref]

    " target="_blank">10.1007/s100649900032">[Crossref]

  34. Natasya, I. D., Larang, M. P., Putri, E. G. G., & Refrizon, R. R. (2022). Upaya Mitigasi Bencana Longsor Jalan Lintas Bengkulu-Kepahiang Berdasarkan Kecepatan Gelombang Geser (Vs). Newton-Maxwell Journal of Physics, 3(1), 33-37. https://doi.org/10.33369/nmj.v3i1.21243">[Crossref]

  35. Nourani, V., Pradhan, B., Ghaffari, H., & Sharifi, S. S. (2014). Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Natural hazards, 71, 523-547. [Crossref]

    " target="_blank">10.1007/s11069-013-0932-3">[Crossref]

  36. Okoli, J., Nahazanan, H., Nahas, F., Kalantar, B., Shafri, H. Z. M., & Khuzaimah, Z. (2023). High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia. Geosciences, 13(2), 34. https://doi.org/10.3390/geosciences13020034">[Crossref]

  37. Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180-197. https://doi.org/10.1016/j.jseaes.2012.12.014">[Crossref]

  38. Pachauri, A. K., Gupta, P. V., & Chander, R. (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology, 36, 325-334. [Crossref]

    " target="_blank">10.1007/s002540050348">[Crossref]

  39. Pourghasemi, H. R., Gayen, A., Park, S., Lee, C. W., & Lee, S. (2018). Assessment of landslide-prone areas and their zonation using logistic regression, logitboost, and naïvebayes machine-learning algorithms. Sustainability, 10(10), 3697. https://doi.org/10.3390/su10103697">[Crossref]

  40. Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7, 725-742. [Crossref]

    " target="_blank">10.1007/s12517-012-0807-z">[Crossref]

  41. Roberts, A. (2001). Curvature attributes and their application to 3D interpreted horizons. First break, 19(2), 85-100. [Crossref]

    " target="_blank">10.1046/j.0263-5046.2001.00142.x">[Crossref]

  42. Roodposhti, M. S., Aryal, J., Lucieer, A., & Bryan, B. A. (2019). Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest. Entropy, 21(1), 1–15. https://doi.org/10.3390/e21010078">[Crossref]

  43. Rotaru, A., Oajdea, D., & Răileanu, P. (2007). Analysis of the landslide movements. International journal of geology, 1(3), 70-79.

  44. Shahabi, H., Khezri, S., Ahmad, B. B., & Hashim, M. (2014). Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena, (115), 55-70. http://dx.doi.org/10.1016%2Fj.catena.2013.11.014">[Crossref]

  45. Shou, K. J., & Lin, J. F. (2020). Evaluation of the extreme rainfall predictions and their impact on landslide susceptibility in a sub-catchment scale. Engineering Geology, 265, 105434. https://doi.org/10.1016/j.enggeo.2019.105434">[Crossref]

  46. Solaimani, K., Mousavi, S. Z., & Kavian, A. (2013). Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geosciences, 6, 2557-2569. [Crossref]

    " target="_blank">10.1007/s12517-012-0526-5">[Crossref]

  47. Sugianto, N., & Refrizon, R. (2021). Struktur Kecepatan Gelombang Geser (Vs) di Daerah Rawan Gerakan Tanah (Longsor) Jalan Lintas Kabupaten Bengkulu Tengah-Kepahiang. Indonesian Journal of Applied Physics, 11(2), 134-142. [Crossref]

    " target="_blank">10.13057/ijap.v11i2.41699">[Crossref]

  48. Suhendra, Z. C. B., & Sugianto, N. (2018). Geological condition at landslides potential area based on microtremor survey. ARPN Journal of Engineering and Applied Sciences, 13(8), 3007-3013.

  49. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124-135.https://doi.org/10.1016/j.catena.2014.02.005"> [Crossref]

  50. van Asch, T. W., Malet, J. P., van Beek, L. P., & Amitrano, D. (2007). Techniques, issues and advances in numerical modelling of landslide hazard. Bulletin de la Société géologique de France, 178(2), 65-88.https://doi.org/10.2113/gssgfbull.178.2.65"> [Crossref]

  51. Wallis, J. R., Schaefer, M. G., Barker, B. L., & Taylor, G. H. (2007). Regional precipitation-frequency analysis and spatial mapping for 24-hour and 2-hour durations for Washington State. Hydrology and Earth System Sciences, 11(1), 415-442. https://doi.org/10.5194/hess-11-415-2007">[Crossref]

  52. Wang, Y., Sun, D., Wen, H., Zhang, H., & Zhang, F. (2020). Comparison of random forest model and frequency ratio model for landslide susceptibility mapping (LSM) in Yunyang County (Chongqing, China). International journal of environmental research and public health, 17(12), 4206. https://doi.org/10.3390/ijerph17124206">[Crossref]

  53. Wei, R., Ye, C., Sui, T., Ge, Y., Li, Y., & Li, J. (2022). Combining spatial response features and machine learning classifiers for landslide susceptibility mapping. International Journal of Applied Earth Observation and Geoinformation, 107, 102681. https://doi.org/10.1016/j.jag.2022.102681">[Crossref]

  54. Xiao, T., Yin, K., Yao, T., & Liu, S. (2019). Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China. Acta Geochimica, 38, 654-669. [Crossref]

    " target="_blank">10.1007/s11631-019-00341-1">[Crossref]

  55. Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35(6), 1125-1138. https://doi.org/10.1016/j.cageo.2008.08.007">[Crossref]

  56. Zhu, L., Wang, G., Huang, F., Li, Y., Chen, W., & Hong, H. (2021). Landslide susceptibility prediction using sparse feature extraction and machine learning models based on GIS and remote sensing. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2021.3054029">[Crossref]


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