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Landslide Susceptibility Mapping of Mountain Roads Based on Machine Learning Combined Model by Hong-qiang Dou; Si-yi Huang; Wen-bin Jian; Hao Wang is a Environmental Science article available to read on EtoBox.

What is Landslide Susceptibility Mapping of Mountain Roads Based on Machine Learning Combined Model about?

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 variations. Then, a single machine learning model can easily become overfitting, thus reducing the accuracy and robustness of the evaluation model. This paper proposes a combined machine-learning model to address the issues. The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes. The mountain roads in the Youxi County, Fujian Province, China were used for the landslide susceptibility mapping. Three most frequently used machine learning techniques, support vector machine (SVM), random forest (RF), and artificial neural network (ANN) models, were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system. The global minimum variance portfolio was utilized to construct a machine learning combined model. 5-fold cr

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Author

Hong-qiang Dou; Si-yi Huang; Wen-bin Jian; Hao Wang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)