# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### Feature Selection
#### 3.5.1 The Feature Selection Framework  
Potential features were assessed for inclusion using the following criteria:  
* Criterion (i): the quality of feature datasets;
* Criterion (ii): the statistical correlation between a feature and the landslide occurrence;
* Criterion (iii): the consistency of the above correlation with the domain knowledge on landslide susceptibility.  
Criterion (i) refers to the spatial and temporal coverage and resolution of the feature data, as well as the accuracy of the data. Good quality of the features datasets is considered crucial to ensuring that the correct data are used in training the ML model, such that the forward predictions made by the model will not be adversely affected by the quality of the training data.  
Criteria (ii) and (iii) ensure the identified features are statistically and physically relevant to landslide susceptibility. As mentioned above, expert knowledge plays a significant role in enhancing the performance of machine learning models as it makes the models more explainable and contributes to the preprocessing of training data to overcome data imbalance and noise problems [14; 15]. By reviewing the consistency of the observed correlation in Criterion (ii) against the existing engineering knowledge, Criterion (iii) allows for the incorporation of domain knowledge, experience and expert judgment on landslide susceptibility into the ML model.  
In this pilot study, a feature selection priority matrix shown in Figure 5 was created for assessment against Criteria (ii) and (iii). Features that fall within the top right-hand quadrant (Quadrant 1) would have a higher priority of being included in the landslide susceptibility models in the pilot study, followed by those falling in the bottom right-hand quadrant (Quadrant 2). Features found in the top or bottom left-hand quadrants (Quadrants 3 and 4) reveal statistical correlations with landslide occurrence that do not tally with the existing engineering knowledge and should be thoroughly reviewed before inclusion.  
The statistical correlation of the features to landslide occurrence (i.e., vertical axis of the feature selection priority matrix in Figure 5) was assessed by means of descriptive analytics. This allows for a preliminary appreciation and understanding of the available data. Figure 6 shows the correlations of the potential features with the density of past landslide occurrences (no./km\({}^{2}\)) using territory-wide data of Hong Kong. The distributions of the area against the categorized features are included in the plots for information. Detailed descriptions about these features and their correlation with landslide occurrence are discussed in the next Section.  
Figure 5: Feature Selection Priority Matrix.  
Figure 6: Statistical Correlation of Landslide Conditioning Factors with Landslide Density: (**a**) Slope gradient; (**b**) Plan curvature; (**c**) Profile curvature; (**d**) Upslope catchment area; (**e**) Normalized Difference Vegetation Index (NDVI); (**f**) Superficial geology (the effect of slope angle to the landslide occurrence is normalized); (**g**) Aspect.