# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### The Modelling Approach  
The landslide susceptibility analysis in this pilot study was treated as a grid-based binary classification problem in machine learning, with year-based data adopted. A grid size of 5 m \(\times\) 5 m is considered, which is consistent with [PERSON] (2016) [2]. [PERSON] et al. (2018) [12] remarked that a grid-based approach is the most common type of mapping unit for landslide susceptibility modelling, which has also been adopted by many others [14].  
One of the major challenges commonly encountered in applying binary classification in landslide susceptibility analyses is the sample bias due to the highly imbalanced dataset, as there is always a scarce proportion of positive value grids (grids with landslide crowns identified) within a study area. The ratio of positive value to negative value grids in the dataset for the study area is in the order of 1:30,000. Such an imbalance can cause a model to be biased towards classifying the susceptible area as safe (i.e., negative value), jeopardizing the accuracy of the minority class prediction. [PERSON] et al. (2021) [15] outlined three typical solutions for overcoming this problem, out of which the data-level technique was commonly adopted in previous studies [10; 11; 16; 17]. This method involves a selection of a 1:1 ratio (or other ratio as appropriate) of landsling data points to non-landsliding data. However, [PERSON] et al. (2018) [12] remarked that such a sampling strategy may prove acceptable only if multiple random selections are performed to evaluate the effects of the sampling and to investigate the natural variability and the uncertainty introduced by the sampling, which is, however, seldom performed. On the other hand, sampling of the data would bias the predicted probabilities of a classifier and lead to a significant over prediction of landslide susceptibility without proper calibration. As the predicted probabilities of the classifier cannot give a realistic indication of the susceptibility, the predicted probability of landslide occurrence of the grids is usually reclassified to yield landslide susceptibility maps with finite classes (and thus limited resolution) of susceptibility.  
In view of the above, this pilot study adopted an alternative approach to handle an imbalanced dataset. Under this approach, while the analysis was still handled as a binary classification problem, no data sampling was applied to avoid biasing the predicted probabilities of a classifier. Instead of taking the classes as predicted by the binary ML classifier (i.e., with or without landslides) directly, the predicted probability of the positive class is taken as the modelling result such that the effect of a sample bias can be avoided. Since the predicted probabilities of the classifier are not biased by data sampling, the predicted probability of the positive class could be taken as a proxy to the landslide probability without calibration. A similar approach was adopted in [PERSON] & [PERSON] (2021) [18] for forecasting  
Figure 4: Extent of the Study Area.  
the number of man-made slope failures in response to rainstorms with a machine learning technique for a slope-based analysis.