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Article Title :

GIS-based MCDA for Gully Vulnerability Mapping Using AHP Techniques

4 (2020)

2

45-63

AHP , Basin characteristics , GIS , Gully Vulnerability Mapping , Multilayer , MCDA

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This paper explores the potentiality of GIS-based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) for gully vulnerability mapping. Multilayer information of basin characteristics, such as drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), slope aspect and land use land cover (LULC), were used in this study to develop a Gully Vulnerability Index (GVI). A weighted approach was implemented on each criterion relative to their inferred influence on gully vulnerability and validated by determining the Consistency Ratio (CR). Findings show a varying magnitude of gully vulnerability across the study area. The low to medium gully vulnerability class was dominant covering a land area of 6557ha (21.25%), and mostly confined to developed areas. Still, it is noteworthy to observe that the severe gully vulnerability class covers a substantial land area of 5825ha (18.88%), which presents a great risk to infrastructural development and human settlements in the study area. The study has a model predictive capability with accuracy rate of 84.62%. The integration of the MCDA and AHP into GIS workflow is an effective approach critical to minimize the limitations associated with gully occurrence analysis, using a singular basin characteristic. The results obtained in the study will equally be important in determining gully risk zones, circumspect urban development, tracking and proper infrastructure construction plans for long-term gully disaster mitigation.

The integration of MCDA and AHP techniques was used for mapping of gully vulnerability.

Drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), Slope aspect, LULC, etc. were used estimation of Gully Vulnerability Index (GVI).

Findings show a varying magnitude of gully vulnerability across the study area.

The model shows good predictive capability with accuracy of 84.62%.

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