Patterns of influence of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction

Yin Xing (), Saipeng Huang (), Jianping Yue (), Yang Chen (), Wei Xie (), Peng Wang (), Yunfei Xiang () and Yiqun Peng ()
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Yin Xing: Suzhou University of Science and Technology
Saipeng Huang: Northeast Petroleum University
Jianping Yue: Hohai University
Yang Chen: Suzhou Institute of Trade and Commerce
Wei Xie: Haixi Institute, Chinese Academy of Sciences
Peng Wang: Suzhou University of Science and Technology
Yunfei Xiang: Nanjing Forestry University
Yiqun Peng: San Jiang University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 118, issue 1, No 29, 709-727

Abstract: Abstract Some landslide susceptibility modeling uses idealized landslide points or buffer circles as landslide boundaries, adding uncertainty to the susceptibility modeling. However, landslide boundaries and their spatial shapes are typically presented as irregular polygonal surfaces, such as semicircles and bumps. To study the influence of different landslide boundaries on modeling uncertainty, 370 landslides and 11 environmental factors in Ruijin were chosen in order to establish landslide boundaries and their frequency ratio correlations with environmental factors. Then, these borders were formed, utilizing, respectively, landslide points, buffer circles, and precisely encoded and drawn polygons. Then, models like Point, Circle, and Polygon-based DBN and RF were built using deep belief network (DBN) and random forest (RF). Finally, the distribution pattern of the susceptibility index and its variability were used, along with the receiver operating characteristic (ROC) accuracy, to analyze the modeling uncertainty. The results indicate that: (1) while correct landslide polygon borders are more successful in ensuring modeling accuracy and dependability, using landslide points or buffer circles as boundaries can increase modeling uncertainty. (2) but the. (3) in the absence of precise landslide borders, the landslide susceptibility results derived by employing points and buffer circles as landslide barriers can reflect the spatial distribution pattern of landslide likelihood in the studied area as a whole.

Keywords: Landslide susceptibility; Landslide boundary shape; Uncertainty analysis; Machine learning; Random forest (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06025-7

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