Article Figures & Data
Figures
- Figure 1
Location of Hailun and the spatial distribution of the gullies.
- Figure 2
Unmanned aerial vehicle photographs of several gullies in the study area.
- Figure 3
Indicators extracted in this study: (a) elevation, (b) slope, (c) aspect, (d) plan curvature, (e) profile curvature, (f) topographic wetness index, (g) soil type, (h) land use, (i) normalized difference vegetation index, (j) precipitation, (k) distance from rivers, and (l) distance from gullies.
- Figure 4
Receiver operator curve (ROC) and area under the curve (AUC) values for the four models ([a] support vector machine [SVM], [b] multilayer perceptron neural network [MLPNN], [c] random forest [RF], and [d] extreme gradient boosting [XGBoost]).
- Figure 5
Relative importance of the indicators for gully erosion prediction.
- Figure 6
Gully erosion susceptibility (GES) maps obtained using the (a) extreme gradient boosting (XG-Boost) and (b) random forest (RF) models.
Tables
- Table 1
Details of the controlling factors used in gully erosion prediction.
- Table 4
Area of each gully susceptibility levels in extreme gradient boosting (XGBoost) and random forest (RF) model.
In this issue
Jump to section
Related Articles
Cited By...
- No citing articles found.