Machine learning and landslide studies: recent advances and applications

Abstract

Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.

Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study

“…A grid size of 5 m × 5 m is considered, which is consistent with Ko & Lo (2016) [2]. Reichenbach 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].…”

“…So far, a wide range of conventional ML and deep learning (e.g., neural networks) algorithms have been developed for classification and regression purposes. They have been used in various landslide studies, yet there is still no consensus on an "optimal" algorithm nor a single "best" algorithm [14]. While Ma et al (2020) [15] considered the use of ML methods in landslide predictions achieve satisfactory performance in general, the use of ensemble learners constructed from a set of base learners was recommended.…”

“…Hyperparameters are parameters that control the learning process of a ML model, the optimal set of hyperparameters to be used varies by case as it is dependent on the algorithm and the dataset involved. Tehrani et al (2021) [14] remarked that hyperparameter tuning plays a significant role in the performance and the predictive ability of an ML model. The tuning of hyperparameters is a process of trial and error, which can be done either systematically (e.g., grid search) or randomly.…”

“…As the conventional statistical approach requires the grouping of data and predefining correlations manually for curve-fitting, the resolution of the analysis is limited and the undertaking of a non-linear multivariate analysis is difficult. Hence, ML techniques become a popular means to model complex landslide problems and a promising predictive performance has been yielded [14]. In light of this, the GEO has recently commenced a pilot study to explore the potential improvements that can be brought to the natural terrain landslide susceptibility model with ML.…”

“…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.…”