# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis  
### Background  
A landslide-prone area may exist on a hillside even though it is not evident from historical landslides. As a large proportion of the land area in Hong Kong consists of natural hillsides, it is of interest to provide an account of the spatial likelihood of landslide occurrence with due consideration of the different local terrain and environmental attributes in a territory-wide sense. Landslide susceptibility mapping can provide a broad categorization of landslide frequency and susceptibility for the zoning of terrains. In practice, depending on the resolution and reliability of the susceptibility mapping, the results can be applied to predicting the number of natural terrain landslides in a rainstorm and undertaking qualitative or quantitative risk assessment [8]. In Hong Kong, natural terrain landslide susceptibility analyses were undertaken at different scales and levels of detail. Table 1 summarizes the details of some notable studies, which can be classified as (a) the conventional statistical approach and (b) the machine learning (ML)-based approach. These approaches are briefly described as follows. 1. Conventional statistical approach: [PERSON] (1998) [9] undertook the first landslide susceptibility analysis for the whole natural terrain of Hong Kong. The historical landslides observed from aerial photographs, together with the slope angle and lithology, were considered. The result provided five susceptibility classes differentiated by one order of magnitude in terms of landslide frequency (0.1 to 1 no. of landslide/km\({}^{2}\)/year). Subsequently, in cognizance of the rainfall's effects on the landslides, [PERSON] & [PERSON] (2016) [2] proposed an updated, higher-resolution, and territory-wide natural terrain landslide susceptibility model by considering rainfall intensity as an additional conditioning factor. Year-based rainfall intensities were adopted. An enhanced resolution of the landslide and terrain data were also used. This updated landslide susceptibility model achieved an overall four to five orders of magnitude in terms of landslide density (no./km\({}^{2}\)). 2. Machine learning (ML)-based approach: [PERSON] & [PERSON] (2002) [10] adopted the logistic multiple regression method to categorize the relative landslide susceptibility of the natural terrain on an outlying island of Hong Kong. Recently, [PERSON] et al. (2021) [11] carried out a territory-wide spatiotemporal modelling of rainfall-induced natural terrain landslides with ML and deep learning algorithms considering storm-based data. Both of these studies adopted grid-based analysis, which is by far the most commonly adopted mapping unit for statistically-based landslide susceptibility analysis [12]. [PERSON] et al. (2021) [13] performed an AI-based territory-wide landslide susceptibility analysis of Hong Kong with an object-based method. Object units of 30 m \(\times\) 30 m formed of 2 m \(\times\) 2 m grids were considered. These ML-based studies have proven to be powerful in learning the association between landslide occurrences and the set of conditioning factors in a mathematical manner.