Bagging-based machine learning algorithms for landslide susceptibility modeling

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

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A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Forests ◽

10.3390/f11010118 ◽

2020 ◽

Vol 11 (1) ◽

pp. 118 ◽

Cited By ~ 6

Author(s):

Viet-Hung Dang ◽

Nhat-Duc Hoang ◽

Le-Mai-Duyen Nguyen ◽

Dieu Tien Bui ◽

Pijush Samui

Keyword(s):

Machine Learning ◽

Random Forest ◽

Landslide Susceptibility ◽

Spatial Prediction ◽

Shallow Landslide ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Conditioning Factors ◽

Susceptibility Modeling

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

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Landslide detection by machine learning on high-resolution DEMs

10.5194/egusphere-egu21-13635 ◽

2021 ◽

Author(s):

Mihai Niculita

Keyword(s):

Machine Learning ◽

High Resolution ◽

Binary Classification ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Single Event ◽

High Resolution Data ◽

Susceptibility Modeling ◽

Study Case ◽

High Level

<p>Machine learning algorithms are increasingly used in geosciences for the detection of susceptibility modeling of certain landforms or processes. The increased availability of high-resolution data and the increase of available machine learning algorithms opens up the possibility of creating datasets for the training of models for automatic detection of specific landforms. In this study, we tested the usage of LiDAR DEMs for creating a dataset of labeled images representing shallow single event landslides in order to use them for the detection of other events. The R stat implementation of the keras high-level neural networks API was used to build and test the proposed approach. A 5m LiDAR DEM was cut in 25 by 25 pixels tiles, and the tiles that overlayed shallow single event landslides were labeled accordingly, while the tiles that did not contain landslides were randomly selected to be labeled as non-landslides. The binary classification approach was tested with 255 grey levels elevation images and 255 grey levels shading images, the shading approach giving better results. The presented study case shows the possibility of using machine learning in the landslide detection on high-resolution DEMs.</p>

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Combining Logistic Regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping

Geocarto International ◽

10.1080/10106049.2021.2022009 ◽

2021 ◽

pp. 1-25

Author(s):

Saeed Alqadhi ◽

Javed Mallick ◽

Swapan Talukdar ◽

Ahmed Ali Bindajam ◽

Tamal Kanti Saha ◽

...

Keyword(s):

Machine Learning ◽

Sensitivity Analysis ◽

Logistic Regression ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Susceptibility Mapping ◽

Machine Learning Algorithms ◽

Landslide Susceptibility Mapping

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Applying Machine Learning Algorithms in Landslide Susceptibility Assessments

Handbook of Neural Computation ◽

10.1016/b978-0-12-811318-9.00024-7 ◽

2017 ◽

pp. 433-457 ◽

Cited By ~ 1

Author(s):

Paraskevas Tsangaratos ◽

Ioanna Ilia

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Landslide Susceptibility Assessment Using an AutoML Framework

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182010971 ◽

2021 ◽

Vol 18 (20) ◽

pp. 10971

Author(s):

Adrián G. Bruzón ◽

Patricia Arrogante-Funes ◽

Fátima Arrogante-Funes ◽

Fidel Martín-González ◽

Carlos J. Novillo ◽

...

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Spatial Databases ◽

Learning Algorithms ◽

Area Under The Curve ◽

Remote Sensing Data ◽

Machine Learning Algorithms ◽

Sources Of Information ◽

Natural Catastrophes ◽

Input Variables

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.

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