Flood Hazard Zonation Using an Integrated GIS–AHP Approach in the Bardaskan Watershed, Khorasan Razavi Province, Iran

Document Type : Original Article

Authors

1 Associate Professor of Geomorphology, Department of Geography, Faculty of Law and Social sciences. Payame Noor University, Tehran, Iran.

2 Assistant Professor of Geomorphology, Department of Geography Education, Farhangian University, Tehran, Iran.

3 Master of Science in Urban Planning, Ministry of Interior, Khorasan Razavi Provincial Government, Bardaskan Municipality, Iran

Abstract

Floods are critical hazards in arid and semi-arid regions, causing substantial damage to settlements and infrastructure, particularly following torrential rainfall and rapid runoff concentration. Consequently, identifying and zoning flood-prone areas plays a pivotal role in risk management and spatial planning. This study aims to evaluate and map flood hazards in the Bardaskan watershed using an integrated approach of Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). A comprehensive set of hydrological, geomorphological, and environmental criteria, including slope, distance from streams, drainage density, land use, soil permeability, and precipitation characteristics, was analyzed. The relative weight of each criterion was determined using the AHP method. By overlaying these spatial layers in a GIS environment, a flood hazard zonation map was generated, classifying the watershed into five categories from very low to very high risk.

The findings reveal that factors associated with the drainage network and precipitation patterns, notably distance from streams and short-duration rainfall events, exert the greatest influence on the spatial configuration of flood hazards. Downstream sections and areas adjacent to main streams exhibit the highest potential for flood occurrence. Model evaluation using the Receiver Operating Characteristic (ROC) curve yielded an Area Under the Curve (AUC) value of 0.782, demonstrating acceptable performance in delineating flood-susceptible zones.

Overall, this research illustrates that integrating GIS and AHP provides an effective framework for spatial analysis of flood hazards, despite limited hydrometric data. The resulting map serves as a robust scientific foundation for runoff management, land-use planning, and mitigating flood damages in Bardaskan.

Keywords

Main Subjects


Alaghmand, S., Beecham, S., & Mousavi, S. J. (2020). Flash flood mitigation in arid and semi-arid regions: A review. Journal of Flood Risk Management, 13(1), e12605. https://doi.org/10.1111/jfr3.12605
Asadi, H., Ghorbani, M., & Pourghasemi, H. R. (2017). Spatial modeling of flood hazard using GIS and multi‑criteria decision analysis: A case study of the Haraz watershed, Iran. Natural Hazards, 87(2), 1185–1205.
Bayati Khatibi, M., & Moghaddam, F. (2026). Modeling flood hazard sensitivity using multi-criteria spatial analysis: A case study of part of the Ghotor River basin, Khoy County. Hydrogeomorphology, 13(46), 41–58. https://doi.org/10.22034/hyd.2025.67046.1791
Bui, D. T., Tsangaratos, P., Nguyen, V. T., Van Liem, N., & Trinh, P. T. (2018). Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility mapping. Catena, 188, 104426. https://doi.org/10.1016/j.catena.2019.104426
Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2017). Flood susceptibility mapping using data-driven models in GIS: A case study of the Poyang County, China. Science of the Total Environment, 579, 149–161. https://doi.org/10.1016/j.scitotenv.2016.11.052
Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied hydrology. New York: McGraw‑Hill.
Costache, R., Arabameri, A., Blaschke, T., Pham, Q. B., Pham, B. T., & Pandey, M. (2021). Flash flood potential mapping using deep learning, bivariate statistics and multi-criteria decision-making methods. Journal of Hydrology, 598, 126306. https://doi.org/10.1016/j.jhydrol.2021.126306
Feldman, A. D. (Ed.). (2000). Hydrologic modeling system HEC-HMS: Technical reference manual. U.S. Army Corps of Engineers.
Fernández, P., Mourato, S., & Moreira, M. (2010). Social vulnerability assessment of flood risk using GIS-based multicriteria decision analysis. Natural Hazards and Earth System Sciences, 10(1), 181–195.
Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H. B., Gróf, G., & Tien Bui, D. (2020). A comparative assessment of flood susceptibility modeling using multi‑criteria decision‑making analysis and machine learning methods. Journal of Hydrology, 573, 311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073
Knebl, M. R., Yang, Z. L., Hutchison, K., & Maidment, D. R. (2005). Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC‑HMS/RAS: A case study for the San Antonio River Basin summer 2002 storm event. Journal of Environmental Management, 75(4), 325–336. https://doi.org/10.1016/j.jenvman.2004.11.024
Kundzewicz, Z. W., et al. (2018). Flood risk and climate change: Global and regional perspectives. Hydrological Sciences Journal, 63(1), 1–28. https://doi.org/10.1080/02626667.2017.1393147
Makhdoum, M. (2014). Foundations of land use planning (regional planning) [In Persian]. Tehran: University of Tehran Press.
Merz, B., Thieken, A. H., & Gocht, M. (2007). Flood risk mapping at the local scale: Concepts and challenges. Natural Hazards and Earth System Sciences, 7(4), 537–551.
Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30. https://doi.org/10.1002/hyp.3360050103
Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536
Pham, B. T., Avand, M., Pourghasemi, H. R., Abbaspour, R. A., & Prakash, I. (2020). GIS-based ensemble soft computing models for flood susceptibility mapping. Journal of Hydrology, 587, 124954. https://doi.org/10.1016/j.jhydrol.2020.124954
Pourghasemi, H. R., Ghorbani, M., & Parvizi, Z. (2012). Flood hazard zonation using geographic information system and analytic hierarchy process (AHP). Journal of Geographical Research, 27(3), 1–16.
Pourghasemi, H. R., Rahmati, O., Moradi, H. R., & Ghanbarian, G. (2018). Landslide and flood susceptibility mapping using machine learning and statistical models. Science of the Total Environment, 598, 431–448. https://doi.org/10.1016/j.scitotenv.2017.11.307
Poursaeid, A., Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2018). Flood susceptibility mapping using a novel ensemble model of frequency ratio and support vector machine. Journal of Hydrology, 563, 476–488. https://doi.org/10.1016/j.jhydrol.2018.06.037
Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2016). Flood susceptibility mapping using GIS-based multi-criteria decision analysis: A comparison of decision tree, random forest, and analytic hierarchy process models. Journal of Hydrology, 538, 325–341. https://doi.org/10.1016/j.jhydrol.2016.04.007
Rezaei Moghaddam, M. H., Mokhtari, D., Rahimpour, T., & Taghizadeh Timourlouei, V. (2025). Flood hazard modeling using the statistical Weight of Evidence (WOE) method in the Azarshahr Chay watershed. Hydrogeomorphology, 12(42), 20–37. https://doi.org/10.22034/hyd.2024.60081.1723
Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
Saaty, T. L. (2005). Theory and applications of the analytic network process. In Decision making with the analytic network process (pp. 1–26). RWS Publications.
Saeidi, A., Pourghasemi, H. R., & Pradhan, B. (2021). Flood susceptibility mapping using GIS‑based multi‑criteria decision analysis: A case study of northern Iran. Natural Hazards, 106(1), 349–370. https://doi.org/10.1007/s11069-020-04459-8
Shafizadeh Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., & Shirzadi, A. (2020). Novel forecasting approaches using machine learning for flood susceptibility mapping. Journal of Environmental Management, 253, 109701. https://doi.org/10.1016/j.jenvman.2019.109701
Soleimani, H., Kazemi, M., & Ahmadpour, F. (2019). The impact of urbanization on runoff regime changes and intensification of urban floods. Journal of Geography and Planning, 23(4), 97–116.
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Flood susceptibility mapping using GIS-based weighting methods. Journal of Hydrology, 527, 72–85. https://doi.org/10.1016/j.jhydrol.2015.04.038
Zhao, G., Pang, B., Xu, Z., Yue, J., & Tu, T. (2021). Mapping flood susceptibility using machine learning models and GIS: A case study. Science of the Total Environment, 776, 145913. https://doi.org/10.1016/j.scitotenv.2021.145913