A rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model∗. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Dai, Yuqian; Bai, Xianfu; Nie, Gaozhong; Huangfu, Gang
• Is Part Of: Geomatics, natural hazards & risk. Volume 13:Issue 1(2022)
• Page Start: 222
• Page End: 248
• Publication Date: 2022-12-31
• Subjects: Assessment model -- casualties -- earthquake-induced landslides -- kilometer grid; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2021.2017022
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract: The accuracy of rapid earthquake assessment and the emergency assessment system for earthquake-induced damages could be substantially enhanced if the casualties triggered by earthquake-induced geological disasters, such as landslides, are subjected to comprehensive scientific evaluation. However, no credible solution for this purpose has been formulated yet. This study suggests a three-step rapid assessment method designed for earthquake-induced landslide casualties based on the GIS and an associated logistic regression model, as follows: (1) Partition of the region to be evaluated as a 1 km × 1 km grid in the GIS, with assignment of a certain amount of population to each of the grid cells as its population attribute. (2) Calculation of the death rate for each grid cell based upon its earthquake-induced landslide susceptibility attribute using the logistic regression model. (3) The earthquake-induced landslide casualties are first determined for each of the kilometer grid cells, and then for the entire region under evaluation. The proposed method was implemented to test the assessment of earthquake-induced landslide casualties in three earthquake-stricken regions. The study reveals the feasibility of the extensibility and applicability of the proposed rapid assessment method for earthquake-induced landslide casualties, and its suitability for similar assessments and calculations of other regions.