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Erosion susceptibility assessment using fuzzy logic and multi-influencing factors combination approach

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Abstract

Soil erosion is one of the most important threats to land productivity, environmental quality, and socioeconomic development. In this study, the combination of fuzzy logic and MIF technique in the ArcGIS environment was employed to determine land susceptibility to water and wind erosion in the watershed lies in the central part of Iran. Applying this approach by seven factors of rainfall, slope, aspect, land use, geology, drainage density, and soil properties characterized lands vulnerability to water erosion, whereas using six factors viz. wind speed, effective rainfall, land use, geology, soil, and slope determined wind erosion susceptibility. MIF through the interrelationship between factors specified factors weights and combining fuzzy layers based on the obtained weights resulted in erosion potential maps. The results indicated that 68.46% of the region was impressed by high and very high wind erosion risk while 31.02% was subject to severe and very severe classes of water erosion susceptibility assessment. The statistics measures of sensitivity, specificity, accuracy, and kappa coefficient values confirmed accurate results of the models. Also, climate change effects on erosion maps were provided by changes in rainfall, temperature, and evaporation. The results revealed severe and very severe water erosion classes decrease by 0.11% in total, while high and very high wind erosion classes increase by 9.17%. So, climate change causes a negligible reduction in water erosion and an obvious increase in wind erosion.

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Acknowledgements

I would like to thank the Natural Resource and Watershed Management Department of Qom province for helping me to provide watershed information for this project.

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  1. Department of Agriculture, Payame Noor University, Tehran, Iran

    Elham Forootan

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Forootan, E. Erosion susceptibility assessment using fuzzy logic and multi-influencing factors combination approach. Arab J Geosci 15, 444 (2022). https://doi.org/10.1007/s12517-022-09598-y

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