Influence of index contribution rate and machine learning models on the susceptibility of Benggang under slope unit

The dataset contains the research data supporting the manuscript titled "Influence of index contribution rate and machine learning models on the susceptibility of Benggang under slope unit". The data includes the following key components:
Evaluation Indicators Data: This comprises 22 evaluation indicators used to assess the susceptibility of Benggang. These indicators include rainfall erosivity, formation lithology, forest height, vegetation coverage, leaf area index, elevation, slope, modified soil adjusted vegetation index, normalized backscatter coefficients of the VV and VH channels, sand content, soil erosion modulus, clay content, erodibility, slope length and slope factor, topographic humidity index, brightness index, coloring index, slope aspect, plane curvature, profile curvature, and hydrodynamic index. The data is sourced from various remote sensing satellites (e.g., Sentinel-1, Sentinel-2), geological surveys, and other environmental datasets.
Slope Unit Division Data: The study area (Huichang County, Jiangxi Province) was divided into slope units using a multi-scale segmentation algorithm. This dataset includes the spatial distribution and characteristics of the slope units, which serve as the basic spatial units for the susceptibility assessment.
Machine Learning Model Results: The dataset contains the prediction results of three machine learning models (logistic regression, XGBoost, and random forest) applied to assess the susceptibility of Benggang. The results include the AUC values, accuracy statistics, and susceptibility zoning maps under different index contribution rates (100%, 95%, 90%, 85%, 80%, 75%, and 70%).
Key Findings: The data supports the key findings of the study, which indicate that the highest prediction accuracy for Benggang susceptibility is achieved when the index contribution rate is 90%. The random forest model demonstrates superior performance with the highest AUC value of 0.849. The dataset also highlights the spatial distribution of high - susceptibility areas in Huichang County, providing valuable insights for disaster prevention and spatial planning in the region.