Incorporating Dynamic Factors in Geological Hazard Risk Assessment: Integrating InSAR Deformation and Rainfall Conditions DOI Creative Commons
Hui Wang,

Jieyong Zhu,

Linying Chen

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 360 - 360

Published: March 22, 2025

Geological hazards, particularly in mountainous regions, represent significant threats to life, property, and the environment. In this study, we focus on Luoping County, Yunnan Province, China, employing SBAS-InSAR technology monitor surface deformation between 8 October 2022 27 September 2024. By integrating InSAR data with 10 static disaster-causing factors, including elevation, slope, aspect, curvature, distance faults, rivers, roads, engineering geological rock groups, geomorphological types, NDVI, hazard susceptibility was assessed using information value (IV) model value–random forest (IV-RF) coupled model. Accuracy validation ROC curves indicated that IV-RF model, integrated data, achieved highest accuracy, an AUC of 0.805. Based evaluation, rainfall intensity introduced as a triggering factor assess risks under four conditions: 10-year, 20-year, 50-year, 100-year return periods. The results demonstrated incorporating significantly improved disaster prediction providing more reliable sustainable risk assessment outcomes. This study underscores critical role technology, combined conditions, enhancing precision assessments, offering scientific basis for prevention mitigation strategies County similar regions.

Language: Английский

Incorporating Dynamic Factors in Geological Hazard Risk Assessment: Integrating InSAR Deformation and Rainfall Conditions DOI Creative Commons
Hui Wang,

Jieyong Zhu,

Linying Chen

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 360 - 360

Published: March 22, 2025

Geological hazards, particularly in mountainous regions, represent significant threats to life, property, and the environment. In this study, we focus on Luoping County, Yunnan Province, China, employing SBAS-InSAR technology monitor surface deformation between 8 October 2022 27 September 2024. By integrating InSAR data with 10 static disaster-causing factors, including elevation, slope, aspect, curvature, distance faults, rivers, roads, engineering geological rock groups, geomorphological types, NDVI, hazard susceptibility was assessed using information value (IV) model value–random forest (IV-RF) coupled model. Accuracy validation ROC curves indicated that IV-RF model, integrated data, achieved highest accuracy, an AUC of 0.805. Based evaluation, rainfall intensity introduced as a triggering factor assess risks under four conditions: 10-year, 20-year, 50-year, 100-year return periods. The results demonstrated incorporating significantly improved disaster prediction providing more reliable sustainable risk assessment outcomes. This study underscores critical role technology, combined conditions, enhancing precision assessments, offering scientific basis for prevention mitigation strategies County similar regions.

Language: Английский

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