# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 4 Conclusions  
For years, the GEO has been maintaining a comprehensive data inventory which supports the state-of-the-art research and technical development work on landslides. Again, these inventories have provided a very good starting point for the GEO's exploratory studies on the application of machine learning to rainfall-based landslide predictions in Hong Kong. The exploratory study presented in this paper formed a machine learning-based landslide susceptibility model for a pilot study area based on the GEO's latest territory-wide natural terrain landslide susceptibility analysis reported in Ko & Lo (2016) [2]. The results of the above pilot study suggested that the predictive performance of ML-based landslide models is promising. In particular, the adoption of ML techniques allows for a systematic method to include additional features for landslide susceptibility analyses. A feature selection framework and a workflow for the machine learning application--which allowed for the incorporation of domain knowledge on landslides for the construction of physically meaningful machine learning models--were developed. With the emergence of more advanced and complex algorithms with time, the use of ML techniques on landslide susceptibility studies is becoming more popular. While they are powerful, the algorithms learn the association between landslide occurrences and the set of conditioning factors in various manners without considering the physical mechanism behind the slope failures. In other words, the use of ML does not guarantee better suscepti  
Figure 13: Actual Landslide Probability by Positive Classification Scores (FS2-24-XGB). bility models that are physically meaningful unless it is applied with the input of sound professional knowledge. In this pilot study, domain knowledge was carefully introduced to ML-based models through critical feature engineering works, a proper selection of suitable algorithms, and a detailed assessment of the model performances. It is evident that the ML models gave fairly accurate landslide susceptibility predictions which could be directly taken as a proxy to landslide probability for practical applications without calibration. In addition, the resolution of the susceptibility map could be enhanced by approximately three orders of magnitude with the introduction of three additional features identified by critically assessing against our feature selection framework. As a pilot study, the analyses carried out focused on the group of the most promising features and algorithms, based on rainfall and landslide data only up to 2008. We believe the models can be further enhanced in various aspects, which includes the consideration of other ML algorithms and the introduction of additional features that fulfill our feature selection criteria. These features will be introduced in a step-wise manner, with a view to maximizing the amount of information gain while maintaining the feature space of the dataset in a reasonable dimension. The dataset will be expanded to cover data of more recent years (especially for those data associated with intense rainfall comparable to 1993 and 2008) for testing the ML models as the current study considered data only up to 2008. The ML models will also be retrained if necessary. Alternative characterizations of the rainfall intensity and NMRR of different durations may be considered. The scale effect and the effect of post-landslide topography may also be explored. Conceptualization, T.K.C.W. and R.W.M.C. ; Data curation, H.W.M.L. ; Formal analysis, H.W.M.L. ; Investigation, H.W.M.L. and F.L.C.L. ; Methodology, F.L.C.L., T.K.C.W. and R.W.M.C. ; Project administration, H.W.M.L., F.L.C.L. and T.K.C.W. ; Resources, T.K.C.W. and R.W.M.C. ; Software, H.W.M.L. ; Supervision, F.L.C.L., T.K.C.W. and R.W.M.C. ; Validation, F.L.C.L. ; Visualization, H.W.M.L. ; Writing--original draft, H.W.M.L. ; Writing--review & editing, F.L.C.L., T.K.C.W. and R.W.M.C. All authors have read and agreed to the published version of the manuscript.