Analysis of Landslide Risk in South Oku Regency, Indonesia
Abstract
Landslides cause significant economic, physical, and environmental losses. This research aims to analyse landslide risk using hazard analysis, vulnerability analysis, regional capacity analysis, and landslide disaster risk analysis. The study was conducted in South OKU Regency, one of the regencies in South Sumatra Province, which frequently experiences landslides. The method used in this research is a survey method. The data includes DEM data, slope types, land cover type maps, rainfall maps, soil type maps, physical infrastructure, economic losses, environmental damage, development plans, regional capacity index, and data from structured interviews with 19 sub-district heads. Landslide analysis uses the weighting and overlay method; vulnerability analysis uses Multi-Criteria Decision Analysis; capacity analysis refers to the Hyogo Framework for Actions; and risk analysis is based on Perka BNPB No. 2 of 2012. The results of the analysis show that the landslide hazard in South OKU Regency is high and spread across more than half of the sub-districts. This landslide hazard is very vulnerable to the condition of vital physical infrastructure, has the potential to cause significant economic losses, and can damage environmental conditions. On the other hand, regional capacity in dealing with landslide danger is categorized as moderate. The risk of landslides in South OKU Regency is in the medium disaster risk class. Therefore, it is necessary to strengthen community capacity and increase preparedness in facing landslide disasters to minimize the risks posed.
Key words: landslides, hazard, vulnerability, capacity, risk
© 2024 Serbian Geographical Society, Belgrade, Serbia.
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Abdelrahman, K., Al-Otaibi , N., Ibrahim, E., & Binsadoon, A. (2021). Landslide susceptibility assessment and their disastrous impact on Makkah Al-Mukarramah urban Expansion, Saudi Arabia, using microtremor measurements. Journal of King Saud University – Science, 33(7), 1-12. https://doi.org/10.1016/j.jksus.2021.101450
Amato, G., Palombi, L., & Raimondi, V. (2021). Data–driven classification of landslide types at a national scale by using Artificial Neural Networks. International Journal of Applied Earth Observations and Geoinformation, 104(12), 1-11. https://doi.org/10.1016/j.jag.2021.102549
Apon, M., Notoka, K., Chang, N. C., Ezung, M., Thong, G., & Walling, T. (2024). Analysis of an anthropogenically-induced landslide with emphasis on geological precursors. Results in Earth Sciences, 1-10. https://doi.org/10.1016/j.rines.2024.100020
Argentin, A.-L., Hauthaler, T., Liebl, M., Robl, J., Hergarten, S., Prasicek, G., & Dabiri, Z. (2022). Influence of rheology on landslide-dammed lake impoundment and sediment trapping: Back-analysis of the Hintersee landslide dam. Geomorphology, 414(7), 1-21. https://doi.org/10.1016/j.geomorph.2022.108363
Boyd, J., Binley, A., Wilkinson, P., Holmes, J., Bruce, E., & Jonathan, K. (2024). Practical considerations for using petrophysics and geoelectrical methods on clay rich landslides. Engineering Geology, 334, 1-14. https://doi.org/10.1016/j.enggeo.2024.107506
Cencetti, C., Rosa, P., & Fredduzzi, A. (2020). Characterization of landslide dams in a sector of the central-northern Apennines (Central Italy). Heliyon, 6(6), 1-20. https://doi.org/10.1016/j.heliyon.2020.e03799
Cobrie, T., & Purnomo, J. (1993). Peta geologi lembar Lahat, Sumatera Selatan Geological map of the Baturaja quadrangle, South Sumatera. Pusat Penelitian dan Pengembangan Geologi.
Dwivedi, D., Saraf, A., & Das, J. (2023). Geoinformatics-based investigation of slope failure and landslide damming of Chenab River, Lahaul-Spiti, Himachal Pradesh, India. Natural Hazards Research, 3(2), 1-10. https://doi.org/10.1016/j.nhres.2023.02.008
Fayaz, M., Meraj, B. G., Khader, S., & Farooq, M. (2022). ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas. Environmental Challenges, 9(12), 1-14. https://doi.org/10.1016/j.envc.2022.100624
Fazeli, S., Haghani, M., Mojtahedi, M., & Rasyidi, T. (2024). The role of individual preparedness and behavioural training in natural hazards: A scoping review. International Journal of Disaster Risk Reduction, 105, 1-59. https://doi.org/10.1016/j.ijdrr.2024.104379
Guo, Z., Ferrer, J., Hürlimann, M., Medina, V., Puig-Polo, C., Yin, K., & Huang, D. (2023). Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China. Geoscience Frontiers, 14(4), 1-21. https://doi.org/10.1016/j.gsf.2023.101542
Gupta, K., & Satyam, N. (2022). Co-seismic landslide hazard assessment of Uttarakhand state (India) based on the modified Newmark model. Journal of Asian Earth Sciences, 8(12), 1-14. https://doi.org/10.1016/j.jaesx.2022.100120
Huang, W. (1962). Petrology. University of California.
Khan, M., & Al Shoumik, B. (2022). Land degradation neutrality concerns in Bangladesh. Soil Security, 9(12), 1-5. https://doi.org/10.1016/j.soisec.2022.100075
Kitamura, A., Yamada, K., Sugawara, D., Sugawara, D., Yokoyama, Y., Miyairi, Y., & Team, H. (2020). Tsunamis and submarine landslides in Suruga Bay, central Japan, caused by Nankai–Suruga Trough megathrust earthquakes during the last 5000 years. Quaternary Science Reviews, 245(9), 1-23. https://doi.org/10.1016/j.quascirev.2020.106527
Li, J., Xiao, L., Bakker, J., Luo, Q., Yu, H., Wu, J., & Lin, Y. (2023). Landslide-impacted soils recover faster biologically than chemically or physically, though recovery also varies with forest type in subtropical China. Soil and Tillage Research, 225. https://doi.org/10.1016/j.still.2022.105529
Li, Y., & Duan, W. (2024). Decoding vegetation's role in landslide susceptibility mapping: An integrated review of techniques and future directions. Biogeotechnics, 2(1), 1-12. https://doi.org/10.1016/j.bgtech.2023.100056
Lin, Q., Lima, P., Steger, S., Glade, T., Jiang, T., Zhang, J., & Wang, Y. (2021). National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data. Geoscience Frontiers, 12(6), 1-15. https://doi.org/10.1016/j.gsf.2021.101248
Liu, Z., L’Heureux, J.-S., Glimsda, S., & Lacasse, S. (2021). Modelling of mobility of Rissa landslide and following tsunami. Computers and Geotechnics, 140(12), 1-25. https://doi.org/10.1016/j.compgeo.2021.104388
Luo, H., Zhang, L., Zhang, L., Dia, J., & Yin, K. (2023). Vulnerability of buildings to landslides: The state of the art and future needs. Earth-Science Reviews, 238. https://doi.org/10.1016/j.earscirev.2023.104329
Marengo, J., Alcantara, E., Cunha, A., Seluchi, M., Nobre, C., Dolif, G., & Moraes, O. (2023). Flash floods and landslides in the city of Recife, Northeast Brazil after heavy rain on May 25–28, 2022: Causes, impacts, and disaster preparedness. Weather and Climate Extremes, 39(3), 1-17. https://doi.org/10.1016/j.wace.2022.100545
Ma, S., Shao, X., Xu, C., Chen, X., Lu, Y., Xia, C., & Yuan, R. (2024). Distribution pattern, geometric characteristics and tectonic significance of landslides triggered by the strike-slip faulting 2022 Ms 6.8 Luding earthquake. Geomorphology, 453, Article 109138. https://doi.org/10.1016/j.geomorph.2024.109138
Matpady, P., Maiya, A., Acharya, K., Anupama, D., Bhagavat, P., Rao, A., & Shetty, J. (2023). The experiences of the landslide survivors from Kodagu District, India: Need for community-engaged village/ward level micro disaster management planning. Natural Hazards Research, 3(3), 1-9. https://doi.org/10.1016/j.nhres.2023.04.005
Matti, S., Cullen, M., Reichardt, U., & Vigfúsdóttir, A. (2023). Planned relocation due to landslide-triggered tsunami risk in recently deglaciated areas. International Journal of Disaster Risk Reduction, 86, 1-19. https://doi.org/10.1016/j.ijdrr.2023.103536
Ningsih, A., & Handayani, W. (2023). Study of the Characteristics of Landslides in South Ogan Komering Ulu Regency. Journal of Geodesy and Geomatics, 19(9), 1-13. http://dx.doi.org/10.12962/j24423998.v19i1.15647
Perera, E. N., Jayawardana, D., Jayasinghe, P., Bandara, R. M., & Alahakoon, N. (2018). Direct impacts of landslides on socioeconomic systems: a case study from Aranayake, Sri Lanka. Geoenvironmental Disasters, 5(11), 1-12. https://doi.org/10.1186/s40677-018-0104-6
Poddar, I., & Roy, R. (2024). Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin. Quaternary Science Advances, 13, 1-24. https://doi.org/10.1016/j.qsa.2023.100150
Pradhan, S., Tol, D., Rosser, N., & Brain, M. (2022). An investigation of the combined effect of rainfall and road cut on landsliding. Engineering Geology, 307(12), 1-16. https://doi.org/10.1016/j.enggeo.2022.106787
Pu, C., Xu, Q., Wang, X., Li, Z., Chen, W., Zhao, K., & Liu, J. (2023). Refined mapping and kinematic trend assessment of potential landslides associated with large-scale land creation projects with multitemporal InSAR. International Journal of Applied Earth Observation and Geoinformation, 118, 1-16. https://doi.org/10.1016/j.jag.2023.103266
Shao, X., & Xu, C. (2022). Earthquake-induced landslides susceptibility assessment: A review of the state-of-the-art. Natural Hazards Research, 2(3), 1-11. https://doi.org/10.1016/j.nhres.2022.03.002
Song, D., Zhou, G., Chen, X., Li, J., Wang, A., Peng, P., & Xue, K. (2021). General equations for landslide-debris impact and their application to debris-flow flexible barrier. Engineering Geology, 288(7). https://doi.org/10.1016/j.enggeo.2021.106154
Sonker, I., Tripathi, J., & Swarnim. (2022). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio method in Sikkim Himalaya. Quaternary Science Advances, 8(10), 1-15. https://doi.org/10.1016/j.qsa.2022.100067
Sørlie, E., Hartnik, L., Tran, Q., Eiksund, G., Thakur, V., Kjennbakken, H., & Degago, S. (2023). Physical model tests of clay-rich submarine landslides and resulting impact forces on offshore foundations. Ocean Engineering, 273, 1-14. https://doi.org/10.1016/j.oceaneng.2023.113966
Sridharan, A., & Gopalan, S. (2022). Assessing vulnerability of elevated cities to earthquake induced landslides based on landslide mobility. Procedia Computer Science, 201(4), 247-254. https://doi.org/10.1016/j.procs.2022.03.034
Tian, N., & Lan, H. (2023). The indispensable role of resilience in rational landslide risk management for social sustainability. Geography and Sustainability, 4(1), 1-14. https://doi.org/10.1016/j.geosus.2022.11.007
Tran, V., Khuc, T., Truong, X., Nguyen, A., & Phi, T. (2024). Application of potential machine learning models in landslide susceptibility assessment: A case study of Van Yen district, Yen Bai province, Vietnam. Quaternary Science Advances, 14, 1-12. https://doi.org/10.1016/j.qsa.2024.100181
Utomo, B., Oktavia, M., Susilo, Y., & Putri, M. (2023). Identification of drought endemic areas in Musi Banyuasin regency. Jurnal Sains Kuwait, 50(2), 1-6. https://doi.org/10.1016/j.kjs.2023.03.002
Utomo, B., Yusmiono, B., Prasetya, A., Julita, M., & Putri, M. (2022). Analysis of the Danger Level of Forest and Land Fires (Forest and Land Fires) in Ogan Ilir Regency, South Sumatra Province. Regional and Environmental Journal, 10(4), 1-11. http://dx.doi.org/10.14710/jwl.10.1.30-41
Wang, J., Liu, L., Zhao, K., & Wen, Q. (2023). Farmers' adoption intentions of water-saving agriculture under the risks of frequent irrigation-induced landslides. Climate Risk Management, 39, 1-14. https://doi.org/10.1016/j.crm.2023.100484
Winter, M., Shearer, B., Palmer, D., Peeling, D., Harmer, C., & Sharpe, J. (2016). The Economic Impact of Landslides and Floods on the Road Network. Procedia Engineering, 143, 1-10. https://doi.org/10.1016/j.proeng.2016.06.168
Wistuba, M., Gorczyca, E., & Malik, I. (2021). Inferring precipitation thresholds of landslide activity from long-term dendrochronological and precipitation data: Case study on the unstable slope at Karpenciny, Poland. Engineering Geology, 294(12), 1-18. https://doi.org/10.1016/j.enggeo.2021.106398
Wu, W., Guo, S., & Shao, Z. (2023). Landslide risk evaluation and its causative factors in typical mountain environment of China: a case study of Yunfu City. Ecological Indicators, 154(8), 1-13. https://doi.org/10.1016/j.ecolind.2023.110821
Xiong, H., Ma, C., Li, M., Tan, J., & Wang, Y. (2023). Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China. Science of The Total Environment, 866, Article 161430. https://doi.org/10.1016/j.scitotenv.2023.161430
Yoshihara, N., Matsumoto, S., Umezawa, R., & Machida, I. (2022). Catchment-scale impacts of shallow landslides on stream water chemistry. Science of the Total Environment, 825(6), 1-10. https://doi.org/10.1016/j.scitotenv.2022.153970
Zhang, R., & Li, J. (2024). Buckling failure analysis and numerical manifold method simulation for high and steep slope: A case study. Geohazard Mechanics, 2(2), 143-152. https://doi.org/10.1016/j.ghm.2024.04.001
Zhang, T.-l., Zhou, A.-g., Sun, Q., Wang, H.-s., Wu, J.-b., & Liu, Z.-h. (2020). Hydrological response characteristics of landslides under typhoon-triggered rainstorm conditions. China Geology, 3(3), 1-7. https://doi.org/10.31035/cg2020028
Zhang, Y., Xiao, Y., Wang, B., Tang, W., Yu, P., Wei, W., & Buah, A. P. (2024). Directivity effect of the spatial distribution of co-seismic landslides affected by near-fault ground motions. Computers and Geotechnics, 170, Article 106263. https://doi.org/10.1016/j.compgeo.2024.106263
Zhou, M., Yuan, M., Yang, G., & Mei, G. (2023). Risk analysis of road networks under the influence of landslides by considering landslide susceptibility and road vulnerability: A case study. Natural Hazards Research, 4(3), 387-400. https://doi.org/10.1016/j.nhres.2023.09.013
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