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

Peymankhah, Peyman; Jelokhani Niaraki, Mohammadreza; Hamzeh, Saeid

doi:10.22059/ije.2026.412878.1912

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

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

Main Subjects

Surface water resources

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