Journal Article

Study on the Uncertainty of Machine Learning Model for Earthquake-Induced Landslide Susceptibility Assessment

Feng, Haixia, Hu, Qingwu, Miao, Zelang

2022

Overview

The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region. However, there are uncertainties in machine learning applications. In this paper, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are used to assess the landslide susceptibility in order to discuss the model uncertainty. The model uncertainty is explained in three ways: landslide susceptibility zoning result, risk area (high and extremely high) statistics, and the area under Receiver Operating Characteristic Curve (ROC). The findings indicate that: (1) Landslides are restricted by influence factors and have the distribution law of relatively concentrated and strip-shaped distribution in space. (2) The percentage of real landslide in risk area is 86%, 87%, 82%, and 61% in SVM, RF, LR, and ANN, respectively. The area under ROC of RF, SVM, LR, and ANN, respectively, is 90.92%, 80.45%, 73.75%, and 71.95%. (3) Compared with the prediction accuracy of the training set and test set from the same earthquake, the accuracy of landslide prediction in the different earthquakes is reduced.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial neural networks / Data processing / Earthquake prediction / earthquake-induced landslide / Earthquakes / Landslides / Landslides & mudslides / Learning algorithms / Machine learning / model uncertainty / Neural networks / prediction / regression analysis / risk / River networks / Seismic activity / Seismic response / Statistical analysis / Support vector machines / Susceptibility / susceptibility assessment / Uncertainty