The susceptibility maps were classified/categorized into five different zones (very low, low, moderate, high and very high) based on the natural break in data. The landslide locations with the index value greater than 0.55 have been considered for the validation of the maps. The assessment of ...
Furthermore, it was found that the ANFIS-ICA model borrows most of its susceptibility pattern and performance from the distance to roads factor, although the total performance of the model is derived from the integration of all the factors. Moreover, this study attested to the advantages of ...
Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment...
Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data, multicollinearity of existing evaluation index factors, and inconsistency of evaluation factors due to regional environmental va
Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The accuracy of machine learning-based LSA often hinges on the ratio of landslide to non-landslide (or positive/negative, P/N) samples. A proper ratio of the P/N samples will significan...
Landslide susceptibility mapping using GIS based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11, 425-439. [87] Kavzoglu, T., Colkesen, I., Sahin, E.K., 2019. Machine learning techniques in landslide susceptibility mapping: a survey and a ...
Machine learning faces two difficulties in the evaluation of landslide susceptibility. One is the objective quantification of evaluation index, and the other is the selection of training sample-0.5pts. For that reason, the frequency ratio method is used to achieve the objective quantification of evalu...
Thus, this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps. These maps in turn, can be valuable tools for evaluating and mitigating landslide hazards. Keywords: Landslide susceptibility mapping, Deep learning model...
selected by employing a multicollinearity test. The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method. The effectiveness of machine learning models was verified through performance assessors. The landslide susceptibility maps were validated ...
Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China[J]. Geoscience Frontiers, 2024, 15(4): 101802. DOI: 10.1016/j.gsf.2024.101802 Citation: Lanbing Yu, Yang Wang, Biswajeet Pradhan. Enhancing landslide ...