Landslide Disaster Mitigation Strategies Using Interpretive Structural Modeling in Talamau District, West Pasaman Regency

Authors

  • Rizki Amelia Sasqia Putri Universitas Negeri Padang
  • Eri Barlian Universitas Negeri Padang
  • Iswandi Umar Universitas Negeri Padang
  • Ratna Wilis Universitas Negeri Padang
  • Edward Saleh Universitas Sriwijaya

DOI:

https://doi.org/10.33394/j-ps.v14i4.22699

Keywords:

Landslide, Landslide susceptibility, Disaster mitigation, Interpretive structural modeling, Talamau district

Abstract

Landslides are a recurring hazard in Talamau District, West Pasaman Regency, affecting communities, settlements, and infrastructure. This study describes recorded landslide occurrences, maps modelled susceptibility for 2015 and 2025, and structures mitigation elements using Interpretive Structural Modeling (ISM). Susceptibility was modelled by integrating slope, rainfall, soil type, geology, and land-use/land-cover (LULC) through a GIS-based weighted overlay. The physical-factor inputs were held constant between the two model runs, while LULC was varied; therefore, the comparison represents changes in modelled susceptibility associated with the LULC input rather than independently verified changes in landslide probability. The area classified as highly susceptible was 14,942.25 ha (36.63%) in 2015 and 16,174.17 ha (39.65%) in 2025. These outputs have not been validated against an independent, spatially referenced landslide inventory. The ISM assessment used eight mitigation elements and a Structural Self-Interaction Matrix agreed upon by three professors during a joint discussion on 3 August 2026. Applying transitive closure to the reachability matrix derived from the reported SSIM yielded E4 as the independent element and E1, E2, E3, E5, E6, E7, and E8 as linkage elements under the stated DPD cut-off. These ISM results describe expert-assessed structural relationships and do not demonstrate the effectiveness of interventions in practice.

References

Ahmad, N., & Qahmash, A. (2021). SmartISM: Implementation and assessment of interpretive structural modeling. Sustainability, 13(16), Article 8801. https://doi.org/10.3390/su13168801

Alam, E., & Ray-Bennett, N. S. (2021). Disaster risk governance for district-level landslide risk management in Bangladesh. International Journal of Disaster Risk Reduction, 59, Article 102220. https://doi.org/10.1016/j.ijdrr.2021.102220

Bozzolan, E., Holcombe, E. A., Pianosi, F., Marchesini, I., Alvioli, M., & Wagener, T. (2023). A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps. Science of the Total Environment, 858, Article 159412. https://doi.org/10.1016/j.scitotenv.2022.159412

Buenahora Ballesteros, C. A., Martínez-Graña, A. M., & Yenes, M. (2024). Influence of cumulative geotechnical deterioration on mass movement at a medium-scale regional analysis (Cortinas Sector, Toledo, Colombia). Land, 13(7), Article 1000. https://doi.org/10.3390/land13071000

Chen, L., Guo, Z., Yin, K., Shrestha, D. P., & Jin, S. (2019). The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan’en County (Hubei, China). Natural Hazards and Earth System Sciences, 19(10), 2207–2228. https://doi.org/10.5194/nhess-19-2207-2019

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

Dieng, M., Mbow, C., Skole, D. L., & Ba, B. (2023). Sustainable land management policy to address land degradation: Linking old forest management practices in Senegal with new REDD+ requirements. Frontiers in Environmental Science, 11, Article 1088726. https://doi.org/10.3389/fenvs.2023.1088726

Fidan, S., Tanyaş, H., Akbaş, A., Lombardo, L., Petley, D. N., & Görüm, T. (2024). Understanding fatal landslides at global scales: A summary of topographic, climatic, and anthropogenic perspectives. Natural Hazards, 120(7), 6437–6455. https://doi.org/10.1007/s11069-024-06487-3

Gaidzik, K., & Ramírez-Herrera, M. T. (2021). The importance of input data on landslide susceptibility mapping. Scientific Reports, 11, Article 19334. https://doi.org/10.1038/s41598-021-98830-y

Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., & Melillo, M. (2020). Geographical landslide early warning systems. Earth-Science Reviews, 200, Article 102973. https://doi.org/10.1016/j.earscirev.2019.102973

Hao, L., van Westen, C., Rajaneesh, A., Sajinkumar, K. S., Martha, T. R., & Jaiswal, P. (2022). Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India. Catena, 216, Article 106363. https://doi.org/10.1016/j.catena.2022.106363

Haryani, Barlian, E., Tela, I. N., Aditia, E., & Putra, A. (2025). GIS-based analysis of slope, landslide and flood hazards, and land use integrated with indigenous knowledge in Nagari Sijunjung, Indonesia. Geographia Technica, 20(2), 336–353. https://doi.org/10.21163/GT_2025.202.21

Knevels, R., Brenning, A., Gingrich, S., Heiss, G., Lechner, T., Leopold, P., Plutzar, C., Proske, H., & Petschko, H. (2021). Towards the use of land use legacies in landslide modeling: Current challenges and future perspectives in an Austrian case study. Land, 10(9), Article 954. https://doi.org/10.3390/land10090954

Lasaiba, M. A., Tetelepta, E. G., & Ansiska, P. (2024). Assessment of landslide vulnerability in urban areas using GIS and remote sensing: A study in Ambon City. Jurnal Geografi, 16(1), 32–50. https://doi.org/10.24114/jg.v16i1.41978

Moazzam, M. F. U., Vansarochana, A., Boonyanuphap, J., Rahman, G., Djueyep, G. P., & Choosumrong, S. (2020). Spatio-statistical comparative approaches for landslide susceptibility modeling: Case of Mae Phun, Uttaradit Province, Thailand. SN Applied Sciences, 2, Article 384. https://doi.org/10.1007/s42452-020-2106-8

Nepal, P., Khanal, N. R., Zhang, Y., Paudel, B., & Liu, L. (2020). Land use policies in Nepal: An overview. Land Degradation & Development, 31(16), 2203–2212. https://doi.org/10.1002/ldr.3621

Nhu, V.-H., Mohammadi, A., Shahabi, H., Ahmad, B. B., Al-Ansari, N., Shirzadi, A., Geertsema, M., Kress, V. R., Karimzadeh, S., Valizadeh Kamran, K., Chen, W., & Nguyen, H. (2020). Landslide detection and susceptibility modeling on Cameron Highlands (Malaysia): A comparison between random forest, logistic regression and logistic model tree algorithms. Forests, 11(8), Article 830. https://doi.org/10.3390/f11080830

Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91. https://doi.org/10.1016/j.earscirev.2018.03.001

Sholichin, M., Othman, F., Prayogo, T. B., & Rahardjo, S. S. P. (2024). Assessing landslide susceptibility and formulating adaptation strategies in the Konto Watershed, East Java, Indonesia. International Journal of Disaster Risk Reduction, 113, Article 104797. https://doi.org/10.1016/j.ijdrr.2024.104797

Silalahi, F. E. S., Pamela, Arifianti, Y., & Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6, Article 10. https://doi.org/10.1186/s40562-019-0140-4

Situmorang, R. O. P., Wijaya, N., Hafsari, T. A., Putra Pasaribu, P. H., & Anggraeni, R. (2025). Governance of housing spatial planning of Semarang City in landslide mitigation and the influence of biophysical, climatic, and socio-economic factors. Planning Malaysia, 23(38), 128–143. https://doi.org/10.21837/pm.v23i38.1783

Sujatha, E. R., & Sridhar, V. (2021). Landslide susceptibility analysis: A logistic regression model case study in Coonoor, India. Hydrology, 8(1), Article 41. https://doi.org/10.3390/hydrology8010041

Tang, Z., Zheng, X., Pan, J., Huang, X., Zhu, L., Wang, N., Xie, M., Yan, G., Wang, C., Wang, Z., Xu, C., & Song, C. (2025). Spatial joint hazard assessment of landslide susceptibility and intensity within a single framework: Environmental insights from the Wenchuan earthquake. Science of the Total Environment, 963, Article 178545. https://doi.org/10.1016/j.scitotenv.2025.178545

Tayebi, S., Jabed, M. A., Ruano, A. L., Lee, G. O., da Silva, P. F., Ahmed, S., Aristizábal G., E. V., Dahal, R. K., Soltani, A., Khan, M. I., Rahman, M. A., Islam, M. A., & Haque, U. (2024). Stakeholder perspectives on landslide triggers and impacts in five countries. Landslides, 21(8), 2033–2043. https://doi.org/10.1007/s10346-024-02270-4

Umar, I., & Dewata, I. (2023). Pendekatan sistem dalam manajemen sumber daya alam. Deepublish.

Umar, I., Dewata, I., & Barlian, E. (2019). Dynamic model of land use change in landslide hazard zones in Tanah Datar District, West Sumatra. IOP Conference Series: Earth and Environmental Science, 314(1), Article 012070. https://doi.org/10.1088/1755-1315/314/1/012070

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

Zaenurrohman, J. A., Permanajati, I., Nuraga, P. B., & Setijadi, R. (2023). Kerentanan gerakan tanah menggunakan analisis data spasial di daerah Karangjambu, Purbalingga. GEOGRAPHY: Jurnal Kajian, Penelitian dan Pengembangan Pendidikan, 11(1), 158–171. https://doi.org/10.31764/geography.v11i1.14380

Zeng, T., Guo, Z., Wang, L., Jin, B., Wu, F., & Guo, R. (2023). Tempo-spatial landslide susceptibility assessment from the perspective of human engineering activity. Remote Sensing, 15(16), Article 4111. https://doi.org/10.3390/rs15164111

Zhao, F., Miao, F., Wu, Y., Gong, S., Zheng, G., Yang, J., & Zhan, W. (2024). Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change. Science of the Total Environment, 949, Article 175059. https://doi.org/10.1016/j.scitotenv.2024.175059

Zulfahmi, Z., Putra, M. H. Z., Sarah, D., Tohari, A., Madiutomo, N., Hartanto, P., & Damayanti, R. (2025). GIS-based landslide susceptibility mapping with a blended ensemble model and key influencing factors in Sentani, Papua, Indonesia. Geosciences, 15(10), Article 390. https://doi.org/10.3390/geosciences15100390

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Published

2026-10-31

How to Cite

Putri, R. A. S., Barlian, E., Umar, I., Wilis, R., & Saleh, E. (2026). Landslide Disaster Mitigation Strategies Using Interpretive Structural Modeling in Talamau District, West Pasaman Regency. Prisma Sains : Jurnal Pengkajian Ilmu Dan Pembelajaran Matematika Dan IPA IKIP Mataram, 14(4), 2688–2704. https://doi.org/10.33394/j-ps.v14i4.22699

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Research Articles