Frontiers in Earth Science (Sep 2021)

Uncertainties of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Effects of Different Machine Learning Models

  • Wenbin Li,
  • Yu Shi,
  • Faming Huang,
  • Haoyuan Hong,
  • Guquan Song

DOI
https://doi.org/10.3389/feart.2021.731058
Journal volume & issue
Vol. 9

Abstract

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For the issue of collapse susceptibility prediction (CSP), minimal attention has been paid to explore the uncertainty characteristics of different machine learning models predicting collapse susceptibility. In this study, six kinds of typical machine learning methods, namely, logistic regression (LR), radial basis function neural network (RBF), multilayer perceptron (MLP), support vector machine (SVM), chi-square automatic interactive detection decision tree (CHAID), and random forest (RF) models, are constructed to do CSP. In this regard, An’yuan County in China, with a total of 108 collapses and 11 related environmental factors acquired through remote sensing and GIS technologies, is selected as a case study. The spatial dataset is first constructed, and then these machine learning models are used to implement CSP. Finally, the uncertainty characteristics of the CSP results are explored according to the accuracies, mean values, and standard deviations of the collapse susceptibility indexes (CSIs) and the Kendall synergy coefficient test. In addition, Huichang County, China, is used as another study case to avoid the uncertainty of different study areas. Results show that 1) overall, all six kinds of machine learning models reasonably and accurately predict the collapse susceptibility in An’yuan County; 2) the RF model has the highest prediction accuracy, followed by the CHAID, SVM, MLP, RBF, and LR models; and 3) the CSP results of these models are significantly different, with the mean value (0.2718) and average rank (2.72) of RF being smaller than those of the other five models, followed by the CHAID (0.3210 and 3.29), SVM (0.3268 and 3.48), MLP (0.3354 and 3.64), RBF (0.3449 and 3.81), and LR (0.3496 and 4.06), and with a Kendall synergy coefficient value of 0.062. Conclusively, it is necessary to adopt a series of different machine learning models to predict collapse susceptibility for cross-validation and comparison. Furthermore, the RF model has the highest prediction accuracy and the lowest uncertainty of the CSP results of the machine learning models.

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