Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling

Abstract

Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau.

A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

“…In comparison with other ML algorithms, it also yields satisfying results (e.g. Trigila et al 2015;Chen et al 2017;Sevgen et al 2019;Karantanellis et al 2021;Liu et al 2021b;Youssef and Pourghasemi 2021;Feng et al 2023). The RF is a suitable algorithm for generating susceptibility maps due to its proven performance, its inherently increased explainability, and user friendliness.…”

A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

“…The number of landslides considered for training with respect to the size of the area of interest varies strongly (e.g. 79 landslides in 49.74 km 2 (Hong et al 2019), 841 in 2765 km 2 (Feng et al 2023), 132 in 33.4 km 2 (Vasu et al 2016)). Nevertheless, the result of the experiment is an important finding, because the lack of landslide inventories in many areas means that there are only limited options for optimising the data basis and should therefore be considered when interpreting the final susceptibility or hazard map.…”

“…Also in comparison with other ML algorithms it yields satisfying results, e.g. Liu et al (2021); Sevgen et al (2019); Chen et al (2017); Trigila et al (2015); Karantanellis et al (2021); Youssef and Pourghasemi (2021); Feng et al (2023). This together with its inherently increased explainability and user friendliness, makes it a suitable algorithm to choose.…”

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

“…Landslides occur as a result of slope instability [65,66]. Elevation and slope angle play an important role in influencing landslide occurrences because slope instability increases with an increase in elevation and slope angle [67,68]. Landslide events in the Baota District happened in slopes with an elevation range of 20 to 30 m and an angle higher than 60 • .…”

“…The areas under the ROC curves (AUCs) were applied to judge the prediction performance. The AUC values range between 0.5 to 1, and a higher value indicates greater performance [67,68,70].…”