THEMATIC MAPPING OF LANDSLIDE SUSCEPTIBILITY IN NUEVA VIZCAYA: ENHANCING DISASTER RESILIENCE AND MANAGEMENT STRATEGIES
DOI:
https://doi.org/10.29121/shodhkosh.v7.i9s.2026.8058Keywords:
Analytical Hierarchy Process, Causative Factors, Geographic Information System, Landslide Susceptibility, Thematic MappingAbstract [English]
The province of Nueva Vizcaya had experienced landslide occurrences almost every year for the past decade, and last year, the municipality of Ambaguio had experienced landslides causing seven deaths, damages to properties, and degradation of environment. This study aimed to map the causative factors of landslide susceptibility in the municipality of Ambaguio, province of Nueva Vizcaya. The purpose of this study is to provide significant information to the Local Government Unit particularly the Municipal Disaster Risk Reduction and Planning Office, to mitigate disasters brought by landslides. The Geographic Information System and Analytical Hierarchy Process tools were utilized for the thematic mapping and weighting, respectively. The vulnerability of landslide was classified into four classes, namely low, moderate, high, and very high susceptibility. The total area of coverage in hectares in each susceptibility class was also determined to map the individual causative factors. The study reveals that slope, soil type, elevation, precipitation, and landcover with relative weights of 24.68%, 18.00%, 15.07%, 14.49%, and 10.89%, respectively, were the most influential factors of landslide susceptibility, whereas distance from roads and rivers with relative weights of 9.99% and 6.87%, respectively were identified as the least influential factors contributing to landslide susceptibility. To avoid reoccurrences of landsliding, reforestation and preservation of the ecosystem is highly recommended. Policies for land utilization and management for disaster risk reduction to be considered by the concerned authority are enforcement of slope-based zoning regulations, promotion of reforestation and forest conservation, adaptation of sustainable farming practices, development of a community-based landslide early warning system, and integration of disaster risk reduction in local land use and development planning.
References
Aafaf, E. J., Barakat, A., & Khellouk, R. (2019). GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6, Article 3. DOI: https://doi.org/10.1186/s40677-019-0119-7
Abay, A., Mebrahtu, G., & Mulugeto, A. (2025). GIS-based landslide susceptibility mapping using frequency ratio method: A case study from Adrigat-Mugulat mountain chains, northern Ethiopia. Scientific African, 28, e02661. https://doi.org/10.1016/j.sciaf.2025.e02661 DOI: https://doi.org/10.1016/j.sciaf.2025.e02661
Alsubal, S., Bared, M., Harahap, I., & Sapari, N. (2019). A review on mechanism of rainwater in triggering landslide. IOP Conference Series: Materials Science and Engineering, 513(1), 012009. https://doi.org/10.1088/1757-899X/513/1/012009 DOI: https://doi.org/10.1088/1757-899X/513/1/012009
Althuwaynee, O., Pradhan, B., & Lee, S. (2016). A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2016.1148282 DOI: https://doi.org/10.1080/01431161.2016.1148282
Arizapa, J., Combalicer, E., & Tiburan, C. (2018). Landslide susceptibility mapping of Pansanjan-Lumban watershed using GIS and analytical hierarchy process. Environmental Science, Geography, Engineering.
Cellek, S. (2022). Linear parameters causing landslides: A case study of distance to the road, fault, drainage, Kocaeli. Kocaeli Journal of Science and Engineering, 6(2). https://doi.org/10.34088/kojose.1117817 DOI: https://doi.org/10.34088/kojose.1117817
Chen, L., Guo, Z., Jin, S., Shrestha, D. P., & Yin, K. (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). https://doi.org/10.5194/nhess-19-2207-2019 DOI: https://doi.org/10.5194/nhess-19-2207-2019
Dariagan, J., Atando, R., & Asis, J. L. (2020). Disaster preparedness of local governments in Panay Island, Philippines. Natural Hazards, 105(2), 1923–1944. https://doi.org/10.1007/s11069-020-04383-0 DOI: https://doi.org/10.1007/s11069-020-04383-0
Eco, R., Aquino, N., Lagmay, D., Alejandrino, A., Bonus, I., Escape, A., & Timbas, C. (2015). Landslide and debris flow susceptibility mapping of Leyte Province, Philippines using remote sensing, numerical modelling, and GIS. Journal of Science and Remote Sensing Society.
Gabor, D. (2023). Elevation angles, soil textures, soil settlements and water holding capacity on landslides: An experimental case study in the Province of Iloilo, Philippines. International Journal of Research and Innovation in Applied Science, 8(3), 79–92. https://doi.org/10.51584/IJRIAS.2023.8302 DOI: https://doi.org/10.51584/IJRIAS.2023.8302
Guo, Z., et al. (2023). Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques: A case study from middle Yellow River catchment. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.1033085 DOI: https://doi.org/10.3389/feart.2022.1033085
Hadmoko, D., Lavigne, F., Sartohado, J., Gomez, C., & Daryono, D. (2017). Spatio-temporal distribution of landslides in Java and the triggering factors. Forum Geografi, 31(1). https://doi.org/10.23917/forgeo.v31i1.3790 DOI: https://doi.org/10.23917/forgeo.v31i1.3790
Hong, H., Pradhan, B., Sameen, M., Kalantar, B., Zhu, A., & Chen, W. (2018). Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides, 15, 753–772. DOI: https://doi.org/10.1007/s10346-017-0906-8
Jain, A., and Singh, L. (2026). Sustainable Inventory Model for Optimizing Greenhouse Inventory Management Through Sustainable Practices. International Journal of Engineering Technologies and Management Research, 13(2), 9–31. https://doi.org/10.29121/ijetmr.v13.i2.2026.1743 DOI: https://doi.org/10.29121/ijetmr.v13.i2.2026.1743
Javier, D., & Kumar, L. (2019). Frequency ratio landslide susceptibility estimation in a tropical mountain region. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W8, 173–179. https://doi.org/10.5194/isprs-archives-XLII-3-W8-173-2019 DOI: https://doi.org/10.5194/isprs-archives-XLII-3-W8-173-2019
Jones, J., Bennett, G., & Abanco, C. (2023). Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines. Natural Hazards and Earth System Sciences, 23(3), 1095–1115. https://doi.org/10.5194/nhess-23-1095-2023 DOI: https://doi.org/10.5194/nhess-23-1095-2023
Kamal, M., Hossain, F., Ahmed, B., Rahman, Z., & Sammonds, P. (2023). Assessing the effectiveness of landslide slope stability by analyzing structural mitigation measures and community risk perception. Natural Hazards, 117, 2393–2418. DOI: https://doi.org/10.1007/s11069-023-05947-6
Knapen, A., Kitutu, M., Poesen, J., Breugelmans, W., Deckers, J., & Muwanga, A. (2004). Landslides in a densely populated county at the footslopes of Mount Elgon (Uganda): Characteristics and causal factors. Geomorphology, 76(1–2), 149–165. DOI: https://doi.org/10.1016/j.geomorph.2005.07.004
Leynes, R., Pioquinto, W., & Caranto, J. (2005). Landslide hazard assessment and mitigation measures in Philippine geothermal field. Geothermics, 34(2), 205–217. https://doi.org/10.1016/j.geothermics.2004.08.002 DOI: https://doi.org/10.1016/j.geothermics.2004.08.002
Liu, Y., Deng, Z., & Wang, X. (2021). The effects of rainfall, soil type and slope on the processes and mechanisms of rainfall-induced shallow landslides. Applied Sciences, 11(24). https://doi.org/10.3390/app112411652 DOI: https://doi.org/10.3390/app112411652
Meten, M., Bhandary, N., & Yatabe, R. (2015). Effect of landslide factor combination on the prediction accuracy of landslide susceptibility maps in the Blue Nile Gorge of Central Ethiopia. Geoenvironmental Disasters, 2, Article 9. DOI: https://doi.org/10.1186/s40677-015-0016-7
Mohan, R., et al. (2011). Landslide susceptibility mapping using frequency ratio method and GIS in south eastern part of Nilgiri District, Tamil Nadu, India. International Journal of Geomatics and Geosciences, 1(4), 951–961.
Murgia, I., et al. (2015). Effects of land cover changes on shallow landslide susceptibility using Slide for MAP software (Mt. Nerone, Italy). Land, 13(10). https://doi.org/10.3390/land13101575 DOI: https://doi.org/10.3390/land13101575
Nakileza, B., & Nedala, S. (2020). Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt. Elgon, Uganda. Geoenvironmental Disasters, 7, Article 27. DOI: https://doi.org/10.1186/s40677-020-00160-0
Nseka, D., et al. (2022). Implications of soil properties on landslide occurrence in Kigezi Highlands of South Western Uganda. Landslides. https://doi.org/10.5772/intechopen.99865 DOI: https://doi.org/10.5772/intechopen.99865
Opiso, E., Puno, G., Alburo, J. L., & Detalla, A. (2015). Landslide susceptibility mapping using GIS and FR method along the Cagayan de Oro-Bukidnon-Davao City route corridor, Philippines. Surveying and Geo-Spatial Information Engineering, 20, 2506–2512. DOI: https://doi.org/10.1007/s12205-015-0182-x
Palmisano, F., Vitone, C., & Cotecchia, F. (2018). Assessment of landslide damage to buildings at the urban scale. Journal of Performance of Constructed Facilities, 32(4). https://doi.org/10.1061/(ASCE)CF.1943-5509.0001201 DOI: https://doi.org/10.1061/(ASCE)CF.1943-5509.0001201
Petley, D. (2012). Global patterns of loss of life from landslides. Geology, 40(10), 927–930. https://doi.org/10.1130/G33217.1 DOI: https://doi.org/10.1130/G33217.1
Quevedo, R., et al. (2023). Land use and land cover as a conditioning factor in landslide susceptibility: A literature review. Landslides, 20, 967–982. https://doi.org/10.1007/s10346-022-02020-4 DOI: https://doi.org/10.1007/s10346-022-02020-4
Rabby, Y., Ishtiaque, A., & Rahman, S. (2020). Evaluating the effects of digital elevation models in landslide susceptibility mapping in Rangamati District, Bangladesh. Remote Sensing, 12(17), 2718. https://doi.org/10.3390/rs12172718 DOI: https://doi.org/10.3390/rs12172718
Rahim, I., Ali, S. M., & Aslam, M. (2018). GIS based landslide susceptibility mapping with application of analytical hierarchy process in District Ghizer, Gilgit Baltistan, Pakistan. Journal of Geoscience and Environment Protection, 6(2), 34–49. https://doi.org/10.4236/gep.2018.62003 DOI: https://doi.org/10.4236/gep.2018.62003
Roodposhti, M., Rahimi, S., & Beglou, M. (2013). PROMETHEE II and fuzzy AHP: An enhanced GIS-based landslide susceptibility mapping. Natural Hazards, 73(1), 34–49. https://doi.org/10.1007/s11069-012-0523-8 DOI: https://doi.org/10.1007/s11069-012-0523-8
Sui, H., Su, T., Hu, R., Wang, D., & Zheng, Z. (2022). Study on the risk assessment method of rainfall landslide. Water, 14(22), 3678. https://doi.org/10.3390/w14223678 DOI: https://doi.org/10.3390/w14223678
Sultana, N., & Tan, S. (2021). Landslide mitigation strategies in southeast Bangladesh: Lessons learned from the institutional responses. International Journal of Disaster Risk Reduction, 62. https://doi.org/10.1016/j.ijdrr.2021.102402 DOI: https://doi.org/10.1016/j.ijdrr.2021.102402
Temme, A. J. (2021). Relations between soil development and landslides. In Hydrology, Chemical Weathering, and Soil Formation (Chap. 9). https://doi.org/10.1002/9781119563952 DOI: https://doi.org/10.1002/9781119563952.ch9
Thanh, L., & De Smedt, F. (2012). Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi District, Thua Thien Hue Province, Vietnam. Environmental Earth Sciences, 66, 1739–1752. https://doi.org/10.1007/s12665-011-1397-x DOI: https://doi.org/10.1007/s12665-011-1397-x
Tubog, M. V., Villahermoza, R. L., & Perong, J. G. (2023). Landslide susceptibility modeling derived from remote sensing, multi-criteria decision analysis, and GIS techniques: A case study in the Southeast Bohol Province, Philippines. Research Square. https://doi.org/10.21203/rs.3.rs-2547208/v2 DOI: https://doi.org/10.21203/rs.3.rs-2547208/v3
Vojtekova, J., & Vojtek, M. (2020). Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: A case study from Slovakia. Geomatics, Natural Hazards and Risk, 11(1), 131–148. https://doi.org/10.1080/19475705.2020.1713233 DOI: https://doi.org/10.1080/19475705.2020.1713233
Vranken, L., Vantilt, G., Van Den Eeckhaut, M., Vandekerckhove, L., & Poesen, J. (2014). Landslide risk assessment in a densely populated hilly area. Landslides, 12, 787–798. https://doi.org/10.1007/s10346-014-0506-9 DOI: https://doi.org/10.1007/s10346-014-0506-9
Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72(1), 1–12. https://doi.org/10.1016/j.catena.2007.01.003 DOI: https://doi.org/10.1016/j.catena.2007.01.003
Zhang, J., et al. (2024). Spatial distribution characteristics and influence factor analysis of landslides: Case study of the Hanwang area in Qinba Mountains. Earthquake Research Advances, 4(3), 100275. https://doi.org/10.1016/j.eqrea.2024.100275 DOI: https://doi.org/10.1016/j.eqrea.2024.100275
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sarilyn B. Raspado, Abigail P. Cid-Andres

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.






















