Spatiotemporal analysis and threshold modeling of rainfall-induced geological disasters in Anhui Province DOI Creative Commons
Bo Wang, Jie Liu,

Gaoping Liu

и другие.

Frontiers in Earth Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 17, 2025

Rainfall-induced geological disasters are widespread in the Jianghuai region of China, endangering human lives and socioeconomic activities. Anhui Province, a hotspot for these disasters, warrants thorough analysis temporal spatial distribution their correlation with rainfall effective forecasting warning. This study divides Province into Dabie Mountains, southern other areas based on different background conditions, establishes threshold warning models each. We reconstructed collection disaster precipitation records data from 2008 to 2023. Using binary logistic regression, we analyzed between factors selected optimal attenuation parameters area, determined critical levels. Results show: (1) Landslides collapses main types, mostly occurring high altitude like concentrated rainy season June - July each year; (2) Rainfall is inducer, both single heavy processes sustained influencing occurrence, through combined effect; (3) Effective significantly correlated day previous 8 days rainfall. The coefficients regions 0.60, 0.66, 0.61, respectively. shows that setting fine tuned better than province wide threshold. With 79% forecast accuracy, it can provide scientific basis meteorological risk Province.

Язык: Английский

Debris flows dynamic risk assessment and interpretable Shapley method-based driving mechanisms exploring – A case study of the upper reach of the Min River DOI Creative Commons
Yufeng He, Mingtao Ding, Yu Duan

и другие.

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113400 - 113400

Опубликована: Март 31, 2025

Язык: Английский

Процитировано

0

Two-decadal evolution of irreversible surface deformation in a coal mining area revealed by improved InSAR observations DOI
Zijing Liu, Haijun Qiu, Shuai Yang

и другие.

CATENA, Год журнала: 2025, Номер 254, С. 108996 - 108996

Опубликована: Апрель 4, 2025

Язык: Английский

Процитировано

0

Dynamic Landslide Susceptibility Mapping on Time-Series InSAR and Explainable Machine Learning: A Case Study at Wushan in the Three Gorges Reservoir Area, China DOI

NaLin,

Kai Ding,

Libing Tan

и другие.

Advances in Space Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Exploring the impact of introducing the TRIGRS physical model into machine learning model on the rainfall-induced shallow landslide-susceptibility assessment DOI
Li Li,

Siyu Liang,

Yue Qiang

и другие.

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(5)

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0

Spatiotemporal analysis and threshold modeling of rainfall-induced geological disasters in Anhui Province DOI Creative Commons
Bo Wang, Jie Liu,

Gaoping Liu

и другие.

Frontiers in Earth Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 17, 2025

Rainfall-induced geological disasters are widespread in the Jianghuai region of China, endangering human lives and socioeconomic activities. Anhui Province, a hotspot for these disasters, warrants thorough analysis temporal spatial distribution their correlation with rainfall effective forecasting warning. This study divides Province into Dabie Mountains, southern other areas based on different background conditions, establishes threshold warning models each. We reconstructed collection disaster precipitation records data from 2008 to 2023. Using binary logistic regression, we analyzed between factors selected optimal attenuation parameters area, determined critical levels. Results show: (1) Landslides collapses main types, mostly occurring high altitude like concentrated rainy season June - July each year; (2) Rainfall is inducer, both single heavy processes sustained influencing occurrence, through combined effect; (3) Effective significantly correlated day previous 8 days rainfall. The coefficients regions 0.60, 0.66, 0.61, respectively. shows that setting fine tuned better than province wide threshold. With 79% forecast accuracy, it can provide scientific basis meteorological risk Province.

Язык: Английский

Процитировано

0