Correlation Analysis of Geological Disaster Density and Soil and Water Conservation Prevention and Control Capacity: A Case Study of Guangdong Province

This study investigates the spatial coupling between geohazard susceptibility and soil conservation capacity in Guangdong Province, China, using integrated spatial analysis and machine learning approaches. Through kernel density estimation, hotspot analysis, principal component analysis (PCA), and t-SNE clustering applied to 11,252 geohazard records and nine soil conservation factors, we identify three critical mechanisms: (1) Topographic steepness (LS factor) constitutes the primary control on geohazard distribution (r = 0.162, p < 0.001), with high-risk clusters concentrated in northeastern mountainous regions (Meizhou-Huizhou-Heyuan); (2) Vegetation coverage (C_mean) mediates rainfall impacts, exhibiting significant risk reduction (r = −0.099, p < 0.001) despite counterintuitive negative correlations with mean rainfall erosivity; (3) Soil conservation effectiveness depends on topographic context, reducing geohazard density in moderate slopes (Cluster 0: 527.04) but proving insufficient in extreme terrain (Cluster 2: LS = 20.587). The emerging role of rainfall variability (R_slope, r = 0.183) highlights climate change impacts.

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Publication Details

Journal
Water
Published
2025-08-25
DOI
https://doi.org/10.3390/w17172527
Citations
1
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
2.42
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article

Correlation Analysis of Geological Disaster Density and Soil and Water Conservation Prevention and Control Capacity: A Case Study of Guangdong Province

Li Tang, Yaping Lü, J. Fu
1 citations
Water
Landslides and related hazards
2.42
article

Correlation Analysis of Geological Disaster Density and Soil and Water Conservation Prevention and Control Capacity: A Case Study of Guangdong Province

Li Tang, Yaping Lü, J. Fu
article en
1 citations

Abstract

This study investigates the spatial coupling between geohazard susceptibility and soil conservation capacity in Guangdong Province, China, using integrated spatial analysis and machine learning approaches. Through kernel density estimation, hotspot analysis, principal component analysis (PCA), and t-SNE clustering applied to 11,252 geohazard records and nine soil conservation factors, we identify three critical mechanisms: (1) Topographic steepness (LS factor) constitutes the primary control on geohazard distribution (r = 0.162, p < 0.001), with high-risk clusters concentrated in northeastern mountainous regions (Meizhou-Huizhou-Heyuan); (2) Vegetation coverage (C_mean) mediates rainfall impacts, exhibiting significant risk reduction (r = −0.099, p < 0.001) despite counterintuitive negative correlations with mean rainfall erosivity; (3) Soil conservation effectiveness depends on topographic context, reducing geohazard density in moderate slopes (Cluster 0: 527.04) but proving insufficient in extreme terrain (Cluster 2: LS = 20.587). The emerging role of rainfall variability (R_slope, r = 0.183) highlights climate change impacts.

WaterVol. 17(17)
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality (CN), Guangzhou Urban Planning Survey & Design Institute (CN), Ministry of Water Resources of the People's Republic of China (CN), Taiyuan University of Technology (CN)
Openalex Percentile: Top 9%
Landslides and related hazards
2.42
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