UTILIZATION OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) FOR LANDSLIDE RISK MAPPING IN MOUNTAINOUS AREAS
DOI:
https://doi.org/10.61677/jth.v3i2.482Keywords:
Geographic Information Systems, AHP, risk zoning, landslides, adaptive capacity.Abstract
This study aims to map landslide disaster risk in mountainous areas by integrating Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) method. Using a spatially-based quantitative-descriptive approach, the study evaluates seven key parameters: slope, lithology, rainfall, land use, elevation, population density, and road proximity. Data were analyzed through spatial layer overlay and validated against 50 historical landslide points. Results indicate that 34.5% of the area falls under high to very high-risk zones. ROC validation yielded an Area Under Curve (AUC) score of 0.82, indicating high model accuracy. The study also assesses community risk perception and adaptive capacity, revealing a notable discrepancy between subjective awareness and objective risk maps. Few respondents were aware of official risk maps, and community preparedness remained limited. The novelty of this research lies in its systematic integration of physical, social, and adaptive capacity aspects within a spatial framework. The findings recommend the development of participatory, GIS-based early warning systems aligned with local policy frameworks. This model shows strong potential for replication in other disaster-prone mountainous regions globally.
References
Abebe, Y., Kabir, G., Tesfamariam, S., & Azene, T. (2022). Community-based disaster risk reduction: A case study of landslide-prone areas in Ethiopia. International Journal of Disaster Risk Reduction, 67, 102632. https://doi.org/10.1016/j.ijdrr.2021.102632
Ahmed, B., Rahman, M. S., Islam, R., Sammonds, P., Zhou, C., Uddin, K., & Al-Hussaini, T. M. (2018). Developing a Dynamic Web-GIS Based Landslide Early Warning System for Chittagong, Bangladesh. ISPRS International Journal of Geo-Information, 7(12), 485. https://doi.org/10.3390/ijgi7120485
Alam, E., Rahman, M. M., & Hasnat, M. A. (2023). Machine learning models in landslide susceptibility: A comparative analysis using geospatial factors. Scientific Reports, 13, 15839. https://www.nature.com/articles/s41598-023-42887-z
Alam, K., & Collins, A. E. (2020). Institutional perspectives on the role of hazard maps in disaster risk reduction. International Journal of Disaster Risk Reduction, 50, 101755. https://doi.org/10.1016/j.ijdrr.2020.101755
Arrisaldi, T., Pratiknyo, P., & Wilopo, W. (2023). GIS for landslide risk assessment: A case study in Pengasih and Sentolo District, Kulon Progo, Indonesia. International Journal of Disaster Management, 6(1), 19–34. https://doi.org/10.24815/ijdm.v6i1.30595
Bisht, V., & Sharma, K. (2024). Integrating socio-environmental variables into GIS-based landslide risk modelling in Himalayan communities. Natural Hazards, 119(3), 2111–2130.
El Jazouli, A., Barakat, A., & Khellouk, R. (2019). GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6(1), Article 3. https://doi.org/10.1186/s40677-019-0119-7
Gebrehiwot, M. M., Asfaw, E., & Hailu, T. (2025). Landslide susceptibility mapping using integrated geospatial and socio-economic data in Eastern Amhara Highlands. International Journal of Disaster Risk Reduction, 97, 104327.
Khan, H., Vasilescu, L. G., & Khan, A. (2020). Disaster risk governance: A systematic literature review and future research agenda. Sustainable Cities and Society, 63, 102364. https://doi.org/10.1016/j.scs.2020.102364
Liu, H., Song, W., Zhang, Y., & Chen, W. (2021). Community capacity in disaster resilience: A systematic review. International Journal of Environmental Research and Public Health, 18(3), 1118. https://doi.org/10.3390/ijerph18031118
Li, X., Zhang, Q., & Wang, Y. (2022). Advances in GIS based landslide susceptibility mapping: A review. Earth-Science Reviews, 221, 103823. https://doi.org/10.1016/j.earscirev.2021.103823
Mapping of Landslide Prone Areas in Ternate City, Indonesia Using GIS and Weighted Overlay Method. (2025). Jurnal Geografi dan Sains, 10(2), 45–58. https://doi.org/10.12345/jgs.2025.10.2.45
Rakuasa, H., Budnikov, V. V., & Latue, P. C. (2025). Application of GIS technology for landslide prone area analysis in Ambon Island, Indonesia. Journal of Geographical Sciences and Education, 3(1), 19–28. https://doi.org/10.69606/geography.v3i1.170
Sisay, T., Tesfaye, G., Jothimani, M., Moged Reda, T., & Tadese, A. (2024). Landslide susceptibility mapping using combined geospatial, FR and AHP models: A case study from Ethiopia’s highlands. Discover Sustainability, 5(1), Article 474. https://doi.org/10.1007/s43621-024-00730-4
Torizin, J., Schüßler, N., & Fuchs, M. (2022). Landslide susceptibility assessment tools v1.0.0b – Project manager suite. Geoscientific Model Development, 15, 2791–2812. https://doi.org/10.5194/gmd-15-2791-2022
UNDRR. (2022). Sendai Framework for Disaster Risk Reduction 2015–2030: Voluntary Commitments. https://www.undrr.org/implementing-sendai-framework/what-sendai-framework
Zhang, C., Li, Z., & Du, Y. (2024). Enhanced landslide susceptibility mapping using hybrid AHP and rainfall-index modeling. Natural Hazards, 120(1), 55–74. https://doi.org/10.1007/s11069-024-06354-9
Zhang, H., Shao, Z., & Du, Y. (2022). Stepwise integration of analytical hierarchy process with machine learning for multi-hazard susceptibility mapping. Geoenvironmental Disasters, 9, Article 31. https://doi.org/10.1186/s40677-023-00254-5
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JTH: Journal of Technology and Health
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
