An optimization on machine learning algorithms for mapping snow avalanche susceptibility

Peyman Yariyan (), Ebrahim Omidvar, Foad Minaei (), Rahim Ali Abbaspour () and John P. Tiefenbacher ()
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Peyman Yariyan: Saghez Branch, Islamic Azad University
Ebrahim Omidvar: University of Kashan
Foad Minaei: Ferdowsi University of Mashhad
Rahim Ali Abbaspour: University of Tehran
John P. Tiefenbacher: Texas State University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 1, No 5, 79-114

Abstract: Abstract Mapping avalanche-prone areas to mitigate damages is important and vital for safety and development planning. New hybrid models are introduced for snow avalanche susceptibility mapping (SASM) in the Zarrinehroud and Darvan watersheds in northwestern Iran. A hybrid of four learning models—radial basis function, multi-layer perceptron, fuzzy ARTMAP (or predictive adaptive resonance theory (ART), and self-organizing map (SOM)—with three statistical algorithms—frequency ratio, statistical index, and weights-of-evidence—and K-means clustering integrated 20 factors and 177 avalanche locations. The areas most likely to produce snow avalanches were identified. The relative importance of the predictive factors was determined by analyzing the information gain ratio (IGR). Slope (average merit (AM) = 0.48055) and LS (AM = 0.00202) were the most and least important factors. Positive predictive value, negative predictive value, sensitivity, specificity, area under the curve (AUC), standard error (SE), mean square error, and root mean square error (RMSE) were used to validate the results of the models. The K-means-SOM hybrid model (AUC = 0.811, SE = 0.0548, RMSE = 0.39005) produced the best results of the hybrid models. This study demonstrates that SASM can help local managers and planners mitigate losses of life and damages caused by avalanches.

Keywords: Snow avalanche susceptibility mapping; Neural network; Hybrid models; GIS; Iran (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05045-5

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