Machine learning for landslides prevention: a survey
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REVIEW
Machine learning for landslides prevention: a survey Zhengjing Ma1 • Gang Mei1 • Francesco Piccialli2 Received: 18 July 2020 / Accepted: 10 November 2020 Ó The Author(s) 2020
Abstract Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention. Keywords Natural disasters Landslides prevention Machine learning Supervised learning Unsupervised learning Deep learning Abbreviations ANN Artificial neural networks CNN Convolutional neural networks DT Decision tree DBN Deep belief networks DEM Digital elevation models ELM Extreme learning machine GIS Geographic information systems GAN Generative adversarial networks GNN Graph neural networks GBDT Gradient boosting decision tree kNN k-nearest neighbors LR Logistic regression LSTM Long short-term memory NB Naive Bayes Please note that a preprint version of this paper has been posted on TechRxiv at: http://dx.doi.org/10.36227/techrxiv. 12546098.v1 & Gang Mei [email protected] & Francesco Piccialli [email protected] 1
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
2
Department of Mathematics and Applications R. Caccioppoli, University of Naples Federico II, Naples, Italy
PCA RF RNN RBN SVM
Principal components analysis Random forest Recurrent neural networks Restricted Boltzmann machine Support vector machine
1 Introduction Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system [42]. Their existence is ascribed to the geological environment and meteorological processes on earth. Some factors, including lithology, slope morphology, and unplanned urban expansions, can predispose slopes to landslides [28, 73]. Severe extreme events caused by climate change, including heavy rainfall and rapid snowmelt, could also trigger landslide occurrences [167]. With climate change has strengthened, the frequency and intensity of landslides are expected to increase rapidly as a consequence. It is quite urgent to understand landslides to predict their occurrences and behavior, and then to adopt appropriate prevention policies and met
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