Journal of Ecohydrology

Journal of Ecohydrology

Evaluation of the Role of Spatial Neighborhood Structures in Flood Susceptibility Zonation Using the Fuzzy Weighting Model (Case Study: Abdollah-Abad Watershed, Semnan)

Document Type : Research Article

Authors
Peyman Peymankhah 1
Mohammadreza Jelokhani Niaraki 2
Saeid Hamzeh 2
1 PhD Student of Remote Sensing and GIS, Faculty of Geography, University of Tehran.
2 Associate Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran.
10.22059/ije.2026.412878.1912
Abstract
Objective: This study aimed to zone the flood susceptibility in the Abdollah‑Abad watershed of Sorkheh County, with an emphasis on evaluating the role of spatial neighborhood structures in improving spatial analyses. Given the limited availability of hydrological data in many watersheds, the use of multi‑criteria analysis and fuzzy logic provides an effective approach for identifying flood‑prone areas.
 
Method: Nine environmental criteria influencing flood occurrence were analyzed in a GIS environment. First, thematic layers were standardized using fuzzy membership functions, and the weights of the criteria were determined through the Analytic Hierarchy Process (AHP). The flood susceptibility map was then generated using the fuzzy Gamma operator. To examine the effects of spatial dependencies, two types of spatial neighborhood structures, distance‑based and boundary‑based were incorporated into the model, and the final map was produced by integrating their outputs.
 
Results: The results indicated that slope and precipitation had the highest influence on flood susceptibility. Comparing the baseline model with the models incorporating neighborhood structures showed that applying spatial neighborhoods reduced spatial noise and increased the spatial continuity of susceptibility zones. The distance‑based structure smoothed spatial patterns by accounting for the gradual influence of adjacent units, whereas the boundary‑based structure enhanced the continuity along the boundaries of hazard classes. The final flood susceptibility map revealed that the moderate class occupies the largest proportion of the watershed (42.24%), followed by the high (26.70%) and low (21.78%) classes, whereas the very high and very low classes represent only 2.35% and 6.93% of the total area, respectively.
Conclusions: The findings demonstrate that integrating fuzzy logic, multi‑criteria decision‑making methods, and spatial neighborhood structures provides an effective framework for flood susceptibility zonation. Considering spatial dependencies among spatial units enhances the coherence of output maps and leads to a more accurate understanding of the spatial pattern of flood susceptibility. This approach can support flood‑risk management and land‑use planning. 
Keywords
Flood susceptibility zonation
Fuzzy AHP
Fuzzy overlay
Boundary-based
Distance-based
Subjects
  • Abedini, M., Sabouri, H., & Pasban, E.H. (2025). Flood hazard zonation and its relationship with land use using the Analytic Network Process (ANP) model (Case Study: Raziehchay Watershed, Ardabil Province). Journal of Urban and Regional Sustainable Development Studies, 6(2), 68–84.
  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
  • Ashfaq, S., Tufail, M., Niaz, A., Muhammad, S., Alzahrani, H., & Tariq, A. (2025). Flood susceptibility assessment and mapping using GIS-based analytical hierarchy process and frequency ratio models. Global and Planetary Change, 251, 104831.
  • Bang, N., & Church Burton, N. (2021). Contemporary flood risk perceptions in England: Implications for flood risk management foresight. Henry Disaster Management Centre, Bournemouth University, Dorset.
  • Bouamrane, A., Derdous, O., Dahri, N., Tachi, S. E., Boutebba, K., & Bouziane, M. T. (2022). A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria). International Journal of River Basin Management, 20(2), 203-213.
  • Chen, Y., & Alexander, D. (2022). Integrated flood risk assessment of river basins: Application in the Dadu river basin, China. Journal of Hydrology, 613, 128456.
  • Cikmaz, B. A., Yildirim, E., & Demir, I. (2025). Flood susceptibility mapping using fuzzy analytical hierarchy process for Cedar Rapids, Iowa. International journal of river basin management, 23(1), 1-13.
  • Dutta, P., & Deka, S. (2024). A novel approach to flood risk assessment: Synergizing with geospatial-based MCDM-AHP model, multicollinearity, and sensitivity analysis in the Lower Brahmaputra Flood Plain, Assam. Journal of Cleaner Production.
  • Farhadi, M., Ahmadi, R., & Rezaei, S. (2019). Flood hazard zonation using GIS and multi‑criteria decision‑making methods. Geographical Research Journal, 34(2), 45–62. [In Persian]
  • Fenglin, W., Ahmad, I., Zelenakova, M. et al. (2023). Exploratory regression modeling for flood susceptibility mapping in the GIS environment. Sci Rep13, 247
  • Gorsevski, Pece V., and Ivica Milevski. "Multilevel Flood Susceptibility Mapping by Fuzzy Sets, Analytical Hierarchy Process, Weighted Linear Combination and Random Forest." ISPRS International Journal of Geo-Information15, no. 4 (2026): 148.
  • Gohil, M., Mehta, D., & Shaikh, M. (2024). An integration of geospatial and fuzzy-logic techniques for flood-hazard mapping. Journal of Earth System Science, 133(2), 1.
  • Husein, Musa., Tariku Takele, Dechasa Diriba, and Shankar Karuppannan. (2025). Flood hazard and risk assessment using GIS and remote sensing in the case of Ziway Lake watershed, central Main Ethiopian Rift. Environmental and Sustainability Indicators,
  • Jelokhani Niaraki, M.R., & Mirzaei, A. (2023). Multi‑criteria Decision Making Analysis in Geographic Information Science. Tehran University Press. [In Persian]
  • Kazemi, M., & Jafarpoor, A. (2024). Identifying the threshold of variables affecting flood zones using machine learning technique (Case study: the downstream region of the Karun River). Water and Soil Management and Modelling, 4(1), 214-232. [In Persian].
  • Mahmoudzadeh, H. and Bakoi, M. (2018). Flood zoning using fuzzy analysis (case study: Sari city). Journal of Natural Environmental Hazards, 7(18), 51-68. [In Persian]
  • Najafi, E. and Karimi Kerdabadi, M. (2020). Flood Risk Evaluation and Zoning using with AHP-Fuzzy Combined Model with Emphasis on Urban Safety (Case Study: Region 1 of Tehran Municipality). Journal of Geography and Environmental Hazards, 9(2), 43-60. [In Persian]
  • Pradhan, B. (2010). Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology, 9(2), 1–18.
  • Rashetnia, S., & Jahanbani, H. (2021). Flood vulnerability assessment using a fuzzy rule-based index in Melbourne, Australia. Sustainable Water Resources Management, 7(2), 351–362.
  • Sami, E. and Ebadi, M. (2024). Urban Flood Hazard Zoning Using Fuzzy-Analytic Network Process (ANP), Case study: Maragheh City. Journal of Urban Ecology Researches, 15(1), 171-186. [In Persian]
  • Smith, K., & Ward, R. (1998). Floods: Physical processes and human impacts. Chichester: Wiley.
  • Wu, J., Chen, X., & Lu, J. (2022). Assessment of long- and short-term flood risk using the multi-criteria analysis model with the AHP-Entropy method in Poyang Lake Basin. International Journal of Disaster Risk Reduction, 75(3), 102968.
  • Xing, Z., Lyu, G., Yao, Y., Liu, Z., & Zhang, X. (2025). Fine-grained analysis and mapping of urban flood susceptibility with interpretable machine learning: A case study of Hefei, China. Journal of Hydrology: Regional Studies, 60, 102501.
  • A, Seejata.K, Chatsudarat.S Chidburee.P, Mahavik.N, Kongmuang.CH, Tantanee.S., 2019. FD Flood Hazard assessment using Fuzzy analytic hierarchy process: A case studt of Bang Rakam model in Thailand. The 40th Asian Conference on Remote Sensing (ACRS 2019) October 14-18, 2019 / Daejeon Convention Center (DCC), Daejeon, Korea.
  • Zhong, M., Lin, K., Tang, G., Zhang, Q., Hong, Y., & Chen, X. (2020). A Framework to Evaluate Community Resilience to Urban Floods: A Case Study in Three Communities. Sustainability, 12(4), 1-21.
Journal of Ecohydrology
Volume 13, Issue 2
Spring 2026
Pages 1268-1295

  • Receive Date 02 April 2026
  • Revise Date 11 May 2026
  • Accept Date 08 June 2026
  • First Publish Date 22 June 2026
  • Publish Date 22 June 2026