Landslide Risk Assessment and Prediction Using Machine Learning: A Comprehensive Review

This passage discusses the use of machine learning approaches for evaluating the risk of landslides and predicting future occurrences. It highlights the importance of analyzing previous landslide occurrences and their characteristics, as well as the impact of hazardous events and their relation to the present situation. The passage mentions that machine learning methods, such as SVMs, neural networks, logistic regression, and random forest, have been used for analyzing landslide risk, particularly using inventory datasets of the study area. It also discusses the integration of machine learning with satellite image classification and information extraction methods, such as object-based and pixel-based approaches. The passage mentions specific machine learning methods used in landslide prediction models, such as SVMs, multi-layer perceptron neural networks, radial basis function neural networks, kernel logistic regression, and logistic model trees. It also discusses the performance of different machine learning methods for landslide detection, such as random forest, SVM, and artificial neural networks. The passage further explores the use of machine learning in landslide susceptibility analysis, landslide displacement prediction, selection of relevant conditioning factors, and landslide area estimation. It highlights the advantages of random forest, such as accuracy and processing speed, and mentions the development of hybrid approaches for landslide prediction. The passage also discusses support vector machines and logistic regression as popular machine learning algorithms used for landslide analysis. Overall, the passage provides an overview of the use of machine learning in landslide risk assessment and prediction.