GIS-Based Analysis of Factors Associated with Flood Risk in Sta. Rosa, Nueva Ecija
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Flooding is one of the most common environmental hazards in low-lying areas of the Philippines, particularly in agricultural municipalities such as Sta. Rosa. This study used Geographic Information Systems (GIS)-based spatial analysis to examine the association between selected physical and environmental factors and the flood risk categories of barangays in Sta. Rosa. Secondary datasets from Project Noah, PAGASA, NAMRIA, and OpenStreetMap were analyzed to evaluate elevation, slope, land use/land cover (LULC), distance from river channels, and rainfall in relation to flood susceptibility. Spearman Rank Correlation was used to determine the strength of association between the selected variables and flood risk categories. Results showed that elevation showed the strongest observed association with flood risk, followed by slope, indicating that low-lying and flatter areas are showed higher observed flood susceptibility. The findings highlight the importance of GIS-based spatial analysis in supporting preliminary flood risk assessment and local disaster mitigation planning.
Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., & Shahabi, H. (2019). Flood susceptibility assessment using hybrid machine learning models. Geocarto International, 34(11), 1252–1272. https://doi.org/10.1080/10106049.2018.1474276
Alcantara-Ayala, I., & Lagmay, A. M. F. (2019). Integration of science and technology for disaster risk reduction in the Philippines. International Journal of Disaster Risk Reduction, 39, Article 101155. https://doi.org/10.1016/j.ijdrr.2019.101155
Bulti, D. T., Abebe, B. G., & Gemeda, D. O. (2021). Flood hazard and risk assessment using GIS and remote sensing in urban areas. Heliyon, 7(1), Article e05868. https://doi.org/10.1016/j.heliyon.2020.e05868
Cabrera, J. S., & Lee, H. S. (2019). Flood-prone area assessment using GIS-based multi-criteria analysis: A case study in Davao Oriental, Philippines. Water, 11(11), Article 2203. https://doi.org/10.3390/w11112203
Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y., Wang, X., & Pradhan, B. (2021). Deep learning for flood susceptibility mapping. Environmental Modelling & Software, 140, Article 105148. https://doi.org/10.1016/j.envsoft.2021.105148
Cinco, T. A., de Guzman, R. G., Ortiz, A. M. D., Delfino, R. J. P., Lasco, R. D., Hilario, F. D., & Ares, E. D. (2016). Observed trends and impacts of tropical cyclones in the Philippines. Atmospheric Research, 168, 1–13.
https://doi.org/10.1016/j.atmosres.2015.09.001
Costache, R., Arabameri, A., & Blaschke, T. (2023). Flood susceptibility assessment using GIS and remote sensing data. Hydrology, 10(7), Article 141. https://doi.org/10.3390/hydrology10070141
Dano, U. L., Balogun, A.-L., Matori, A. N., & Wan Yusouf, K. (2019). Flood susceptibility mapping using GIS-based analytic hierarchy process. Journal of King Saud University – Science, 31(4), 1318–1326. https://doi.org/10.1016/j.jksus.2018.03.006
Department of Agriculture – Bureau of Soils and Water Management. (2020). Soil characteristics and land use map of Nueva Ecija.
Jamali, B., Bach, P. M., Cunningham, L., & Deletic, A. (2020). A cellular automata approach for flood modeling using GIS. Journal of Hydrology, 587, Article 124951.
https://doi.org/10.1016/j.jhydrol.2020.124951
Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., & Pradhan, B. (2019). Flood susceptibility modeling using multi-criteria decision-making and machine learning methods. Journal of Hydrology, 573, 311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073
Kourgialas, N. N., & Karatzas, G. P. (2017). Flood management and GIS modeling of flood hazard areas. Hydrological Sciences Journal, 62(2), 212–225. https://doi.org/10.1080/02626667.2016.1174333
Lagmay, A. M. F., Racoma, B. A., Aracan, K. A., Alconis-Ayco, J., & Saddi, I. L. (2017). Disseminating near-real-time hazards information and flood maps in the Philippines through Web-GIS. Journal of Environmental Sciences, 59, 13–23. https://doi.org/10.1016/j.jes.2017.03.013
Li, Y., Zhang, Q., Yao, J., & Wang, H. (2022). Climate variability and increasing flood risks. Science of the Total Environment, 843, Article 154321. https://doi.org/10.1016/j.scitotenv.2022.154321
Llanes, F. V., Eco, R., Herrero, T. M., Briones, J. B. L., Escape, C. M., Sulapas, J. J., Galang, J. A. M., Ortiz, I. J., Sabado, J. M., Lagmay, A. M., & Rodolfo, R. (2022). Practices in disaster mitigation in the case of the 2015 Typhoon Koppu debris flows in Nueva Ecija, Philippines. Natural Hazards, 114, 665–690. https://doi.org/10.1007/s11069-022-05407-7
Mines and Geosciences Bureau. (2022). Geohazard assessment: Municipality of Sta. Rosa, Nueva Ecija. Department of Environment and Natural Resources. http://databaseportal.mgb.gov.ph/
Montefrio, M. J. F. (2020). State–society relations in disaster risk reduction in the Philippines. World Development, 134, Article 105043. https://doi.org/10.1016/j.worlddev.2020.105043
Nolasco-Javier, D., Kumar, L., & Tengonciang, A. M. (2021). Flood susceptibility mapping using GIS and machine learning in Metro Manila, Philippines. Applied Geography, 133, Article 102492. https://doi.org/10.1016/j.apgeog.2021.102492
Paringit, E. C., Santillan, J. R., & Makinano-Santillan, M. (2017). Nationwide flood hazard mapping using LiDAR technology in the Philippines. Philippine Journal of Science, 146(3), 219–230.
Philippine Atmospheric, Geophysical and Astronomical Services Administration. (2023). Climate change in the Philippines: Observed trends and projections. https://pubfiles.pagasa.dost.gov.ph/
Philippine Atmospheric, Geophysical and Astronomical Services Administration. (n.d.). Climatological normals.
https://www.pagasa.dost.gov.ph/climate/climatological-normals
Pourghasemi, H. R., Kariminejad, N., & Pradhan, B. (2020). Flood susceptibility mapping using GIS-based machine learning models. Science of the Total Environment, 709, Article 136151. https://doi.org/10.1016/j.scitotenv.2019.136151
Rahmati, O., Zeinivand, H., & Besharat, M. (2016). Flood hazard zoning using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 7(3), 1000–1017.
https://doi.org/10.1080/19475705.2015.1045043
Rubio, C. J., Yu, I. S., Kim, H. Y., & Jeong, S. M. (2020). Index-based flood risk assessment for Metro Manila. Water Supply, 20(3), 851–866. https://doi.org/10.2166/ws.2020.020
Santillan, J. R., Makinano-Santillan, M., & Paringit, E. C. (2016). High-resolution flood hazard mapping using LiDAR data in the Philippines. Journal of Flood Risk Management, 9(4), 345–356. https://doi.org/10.1111/jfr3.12137
Siddayao, G. P., Valdez, S. E., & Fernandez, P. L. (2014). Analytic hierarchy process (AHP) in spatial modeling for floodplain risk assessment. International Journal of Machine Learning and Computing, 4(5), 450–457.
https://doi.org/10.7763/IJMLC.2014.V4.453
Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping using GIS and AHP. ISPRS International Journal of Geo-Information, 9(12), Article 720. https://doi.org/10.3390/ijgi9120720
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review. Environmental Modelling & Software, 90, 201–216.
https://doi.org/10.1016/j.envsoft.2017.01.006
Tian, H., Xu, K., & Tian, Y. (2020). Flood susceptibility mapping using support vector machine and GIS. Water, 12(2), Article 430. https://doi.org/10.3390/w12020430
Zhang, H., Chen, X., & Chen, J. (2019). Flood risk assessment using integrated GIS and remote sensing techniques. International Journal of Disaster Risk Reduction, 34, 45–54.
