A step beyond susceptibility: an adaptation of risk framework for monetary risk estimation of gully erosion

Omid Asadi Nalivan (), Ziaedin Badehian (), Majid Sadeghinia (), Adel Soltani (), Iman Islami () and Ali Boustan ()
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Omid Asadi Nalivan: Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR)
Ziaedin Badehian: Lorestan University
Majid Sadeghinia: Ardakan University
Adel Soltani: Payame Noor University
Iman Islami: Tarbiat Modares University
Ali Boustan: Islamic Azad University, Kerman Branch

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 2, No 22, 1684 pages

Abstract: Abstract In an effort to improve the previous gully susceptibility assessments in Iran, we attempted to conglomerate the notions of susceptibility, vulnerability, and exposure associated with gully occurrence across the northeast of the Golestan province in Iran to test the feasibility of the provided risk framework. The adaptive neuro-fuzzy inference system (ANFIS) and its ensemble with the imperialist competitive algorithm (ANFIS-ICA) were adopted to assess gully susceptibility. Based on various performance metrics, including Pierce's skill score, Heidke's skill score, Gilbert's skill score, and the area under the receiver operating characteristic curve, the ANFIS-ICA model with the respective values of 0.64, 0.637, 0.47, and 0.887 evidently outperformed the solitarily used ANFIS model due to being fused to a robust optimization algorithm. The results of susceptibility assessment revealed that about 24% of the study area falls within the highly susceptible zone to gully occurrences which stems from the interactive role of such factors as the red relief image map (RRIM), valley depth, average annual rainfall, distance from roads, and distance from streams. Moreover, the RRIM factor provided promising multi-featured morphological information that boosted the pattern recognition power of the susceptibility model. Gully risk assessment results indicated that approximately 6% of the area falls within the high-risk zone. The highest average values of the monetary risk mainly pertain to residential areas, moderately dense forests, orchards, dense forests, and roads, which would impose a sizable risk to the region in case of crisis. Superimposition of the susceptibility map over the monetary risk map showed that their shared areal coverage only accounts for 17.7%, and there is an 82.3% difference which, in turn, indicated that considering the risk map as an alternative to the previous susceptibility maps would considerably change the spatial allocation of the mitigation measures. Graphical abstract

Keywords: ANFIS; Imperialistic competitive algorithm; Vulnerability; Exposure; Monetary risk (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05110-z

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