Landslide Susceptibility Mapping in Quezon, Nueva Vizcaya Using GIS: An Enhancement

  • Sarilyn R. Lopez
  • Raziel A. Lawayan
  • Lanie Lyn T. Padi
Keywords: landslide, enhancement, factors, enhanced landslide susceptibility map, AHP, GIS, LSM


Creating a landslide susceptibility map is imperative for effectively managing the landslide hazard, reducing property damage and loss of life. Like many other mountainous places, the municipality of Quezon has also experienced landslides, resulting in fatalities, injuries, and property destruction. Hence, this study was conducted to generate an enhanced landslide susceptibility map using a GIS-based spatial multicriteria approach. Seven causative factors, including slope, rainfall, soil type, land cover, elevation, distance from the road, and distance from the river, were selected for the present assessment. An enhanced landslide susceptibility map was generated by integrating AHP and GIS techniques and was categorized into four susceptibility classes: very low, low, moderate, and high. The result presented in the study illustrates that there was a disparity between the two maps. The enhanced map shows a more detailed representation of truly vulnerable areas. Specifically, 3589.64 hectares are identified as very low susceptible, 5690.36 hectares are low susceptible, while 5993.09 hectares and 5792.80 hectares are categorized as moderately and highly susceptible to landslides. The results presented in the study indicate that Barangay Maasin, Runruno, and Calaocan, and some parts of Maddiangat, Baresbes, Buliwao, and Darubba, are identified as at high-risk areas. On the other hand, Barangay Dagupan and Bonifacio are at moderate risk, while Barangay Nalubbunan, Caliat, and Aurora are at low risk. Furthermore, the validation conducted by geotagging and field survey yields positive results, indicating that the validated points aligned on the enhanced LSM. The enhanced LSM serves as a valuable tool for disaster preparedness and enables local agencies to implement targeted measures to mitigate landslide risks, especially in high-risk places.


Addis, A. (2023). GIS‐Based Landslide Susceptibility Mapping Using Frequency Ratio and Shannon Entropy Models in Dejen District, Northwestern Ethiopia. Journal of Engineering, 2023(1), 1062388.
Ali, S., Biermanns, P., Haider, R., & Reicherter, K. (2018). Landslide susceptibility mapping by using GIS along the China–Pakistan economic corridor (Karakoram Highway), Pakistan. Nat. Hazards Earth Syst. Sci, 11, 131-148.
Alsubal, S., bin Sapari, N., Harahap, I. S., & Al-Bared, M. A. M. (2019, April). A review on mechanism of rainwater in triggering landslide. In IOP Conference Series: Materials Science and Engineering (Vol. 513, No. 1, p. 012009). IOP Publishing.
Althuwaynee, O. F., 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, 37(5), 1190-1209.
Arca, M. C. Q., & Lorenzo, G. A. (2018). Landslide hazard mapping using limit equilibrium method with GIS application of roadway traversing mountain slopes: The case of Kitaotao Bukidnon, Philippines. Journal of Nepal Geological Society, 55(1), 93-101.
Arizapa, J. L., Combalicer, E. A., & Tiburan Jr, C. L. (2015). Landslide Susceptibility Mapping of Pansanjan-Lumban Watershed using GIS and Analytical Hierarchy Process. Ecosystems and Development Journal, 5(3), 23-32.
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.
Eco, R. N., Aquino, D. T., Lagmay, A. M. F., Alejandrino, I., Bonus, A. A., Escape, C. M., ... & Timbas, N. L. (2015). Landslide and debris flow susceptibility mapping of Leyte Province, Philippines using remote sensing, numerical modelling, and GIS. Journal of the Philippine Geoscience and Remote Sensing Society, 1(1), 53-71.
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), 3.
Guo, Z., Tian, B., Li, G., Huang, D., Zeng, T., He, J., & Song, D. (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, 1033085.
Hadmoko, D. S., Lavigne, F., Sartohadi, J., Gomez, C., & Daryono, D. (2017). Spatio-Temporal Distribution of Landslides in Java and the Triggering Factors. Forum Geogra, 31(1).
Hong, H., Pradhan, B., Sameen, M. I., Kalantar, B., Zhu, A., & Chen, W. (2018). Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides, 15, 753-772.
Javier, D. N., & 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, 42, 173-179.
Jones, J. N., Bennett, G. L., Abancó, C., Matera, M. A., & Tan, F. J. (2023). Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines. Natural Hazards and Earth System Sciences, 23(3), 1095-1115.
Knapen, A., Kitutu, M. G., Poesen, J., Breugelmans, W., Deckers, J., & Muwanga, A. (2006). Landslides in a densely populated county at the footslopes of Mount Elgon (Uganda): characteristics and causal factors. Geomorphology, 73(1-2), 149-165.
Lindelöf, L., & Åberg, I. (2014). Landslide survey, Mamuyod, the Philippines. DiVA portal.
Meten, M., PrakashBhandary, N., & Yatabe, R. (2015). Effect of landslide factor combinations on the prediction accuracy of landslide susceptibility maps in the Blue Nile Gorge of Central Ethiopia. Geoenvironmental Disasters, 2, 9.
Nseka, D., Kakembio, V., Mugagga, F., Semakula, H., Opedes, H., Wasswa, H., & Ayesiga, P. (2022). Implications of soil properties on landslide occurrence in Kigezi highlands of South western Uganda. In Y. Zhang & Q. Cheng (Eds.), Landslides. IntechOpen.
Opiso, E. M., Puno, G. R., Alburo, J. L. P., & Detalla, A. L. (2016). Landslide susceptibility mapping using GIS and FR method along the Cagayan de Oro-Bukidnon-Davao City route corridor, Philippines. KSCE Journal of Civil Engineering, 20, 2506-2512.
PAGASA (Philippine Atmospheric, Geophysical and Astronomical Services Administration). (2022). Climate Change in the Philippines.
Palmisano, F., Vitone, C., & Cotecchia, F. (2018). Assessment of landslide damage to buildings at theurban scale. Journal of Performance of Constructed Facilities, 32(4), 04018055.
Petley, D. (2012). Global patterns of loss of life from landslides. Geology, 40(10), 927-930.
Pioquinto. W. P. C., et al. (2010). Landslide hazard assessment and mitigation measures in Philippine geothermal field. In Proceedings World Geothermal Congress 2010. Bali, Indonesia, 25-29 April 2010.
Rabby, Y. W., Ishtiaque, A., & Rahman, M. S. (2020). Evaluating the effects of digital elevation models in landslide susceptibility mapping in Rangamati District, Bangladesh. Remote Sensing, 12(17), 2718.
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.
Ram Mohan, V., Jeyaseelan, A., Raj, T. N., Narmatha, T., & Jayaprakash, M. (2011). Landslide susceptibility mapping using frequency ratio method and GIS in south eastern part of Nilgiri District, Tamilnadu, India. International Journal of Geomatics and Geosciences, 1(4), 951-961.
Roodposhti, M. S., Rahimi, S., & Beglou, M. J. (2014). PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Natural Hazards, 73, 77-95.
Sui, H., Su, T., Hu, R., Wang, D., & Zheng, Z. (2022). Study on the risk assessment method of rainfall landslide. Water, 14(22), 3678.
Sultana, N., & Tan, S. (2021). Landslide mitigation strategies in southeast Bangladesh: Lessons learned from the institutional responses. International Journal of Disaster Risk Reduction, 62, 102402.
Temme, A. J. (2021). Relations between soil development and landslides. In A. Hunt, M. Egli, & B. Faybishenko (Eds.), Hydrogeology, chemical weathering, and soil formation (pp. 177-185). American Geophysical Union.
Tubog, M. V., Villahermosa, 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. Preprint.
Vojteková, 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.
Vranken, L., Vantilt, G., Van Den Eeckhaut, M., Vandekerckhove, L., & Poesen, J. (2015). Landslide risk assessment in a densely populated hilly area. Landslides, 12, 787-798.
How to Cite
Lopez, S. R., Lawayan, R. A., & Padi, L. L. T. (2024). Landslide Susceptibility Mapping in Quezon, Nueva Vizcaya Using GIS: An Enhancement. European Journal of Science, Innovation and Technology, 4(3), 141-177. Retrieved from