Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors
On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2635 |
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| author | Ruizeng Wei Yunfeng Shan Lei Wang Dawei Peng Ge Qu Jiasong Qin Guoqing He Luzhen Fan Weile Li |
| author_facet | Ruizeng Wei Yunfeng Shan Lei Wang Dawei Peng Ge Qu Jiasong Qin Guoqing He Luzhen Fan Weile Li |
| author_sort | Ruizeng Wei |
| collection | DOAJ |
| description | On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km<sup>2</sup>, predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. |
| format | Article |
| id | doaj-art-e0225f41c12c4c3dbf48a7148107bc33 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e0225f41c12c4c3dbf48a7148107bc332025-08-20T03:36:22ZengMDPI AGRemote Sensing2072-42922025-07-011715263510.3390/rs17152635Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling FactorsRuizeng Wei0Yunfeng Shan1Lei Wang2Dawei Peng3Ge Qu4Jiasong Qin5Guoqing He6Luzhen Fan7Weile Li8Guangdong Key Laboratory of Electric Power Equipment Reliability, Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaGuangdong Key Laboratory of Electric Power Equipment Reliability, Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaGuangdong Key Laboratory of Electric Power Equipment Reliability, Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaOn 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km<sup>2</sup>, predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment.https://www.mdpi.com/2072-4292/17/15/2635extreme rainfallrainfall-induced landslidescluster landslideslandslide susceptibility mappingdistribution patternconditioning factor |
| spellingShingle | Ruizeng Wei Yunfeng Shan Lei Wang Dawei Peng Ge Qu Jiasong Qin Guoqing He Luzhen Fan Weile Li Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors Remote Sensing extreme rainfall rainfall-induced landslides cluster landslides landslide susceptibility mapping distribution pattern conditioning factor |
| title | Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors |
| title_full | Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors |
| title_fullStr | Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors |
| title_full_unstemmed | Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors |
| title_short | Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors |
| title_sort | clustered rainfall induced landslides in jiangwan town guangdong china during april 2024 characteristics and controlling factors |
| topic | extreme rainfall rainfall-induced landslides cluster landslides landslide susceptibility mapping distribution pattern conditioning factor |
| url | https://www.mdpi.com/2072-4292/17/15/2635 |
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