# 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 Machine Learning-Based Modelling
#### 3.6.1 Preprocessing and Resampling of Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Feature** & **Description** & **Criterion (\#)** & **Criterion (\#)** & **Criterion (\#)** & **Criterion (\#)** \\ \hline \multirow{4}{*}{Supercritical Geology} & \multirow{4}{*}{*} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & * Based on 120,000 superficial and solid & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & * Areas overlain by colluvium & * & Statistical correlation is contrary to the domain \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{4}{*}{Aspect} & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & * Crosses direction of the slope & * & * Crosses direction of the slope & * & * Crosses direction of the slope \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._Data preprocessing refers to the preparation of data for model construction. Key actions include the cleansing of data, the encoding of categorical data, and the resampling of data for model training and evaluation. Data cleansing forms part of the feature engineering work, which involves the removal of null or undesirable data from the dataset to ensure only representative and unbiased data are fed into the models. For instance, data associated with rainfall intensities greater than 0.18 for 4-h normalized maximum rolling rainfall (NMRR) or beyond the range of 0.025-0.3 for 24-h NMRR were removed since only a very small portion of the pilot study area (about 0.3% from year 1985 to 2008) had encountered these extreme rainfall intensities in rare events, such that the associated data would not be representative enough for incorporation. The data points associated with the extreme values of the plan and profile curvatures were discarded for a similar reason. The only categorical data involved in the pilot study, the lithological data, was encoded with one-hot encoding for processing in the ML algorithms. The resampling of the dataset along with the modelling is illustrated in Figure 8. The dataset was resampled into a training dataset, a validation dataset, and a testing dataset. The testing dataset for evaluating the models' performance comprised (1) all the data from years 1993 and 2007, and (2) 10% of the data randomly selected from the remaining 22 years in a stratified manner. Stratified random sampling is a commonly adopted sampling technique in which the data are divided into smaller groups or strata and are then randomly selected from each of the strata by the same proportion. The data were stratified based on landslide occurrences in the pilot study such that the ratio of landsling to non-landsliding data in each of the datasets could be maintained. Figure 7: Available Dataset in a Grid. The two sets of testing data are referred to as Testing Data 1 (TD1) and Testing Data 2 (TD2). While TD2 pertained to the type of testing data commonly adopted in other similar studies, TD1 comprised data that possess unseen rainfall patterns during the model construction such that it served as a more stringent test of the models' ability to make forward predictions. Of note, the intensity of rainfall in the year 1993 is one of the highest among the 24 years, whilst that in year 2007 is a moderate one. The remaining data served as the input datasets for the ML model construction as discussed in the next section.