scholarly journals Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms

2019 ◽  
Author(s):  
Husam Abdulrasool Hammadeh Al-Najjar ◽  
Bahareh Kalantar ◽  
Biswajeet Pradhan ◽  
Vahideh Saeidi
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Conditioning Factor

scholarly journals GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

2021 ◽  
Vol 12 (2) ◽  
pp. 857-876
Author(s):  
Sk Ajim Ali ◽  
Farhana Parvin ◽  
Jana Vojteková ◽  
Romulus Costache ◽  
Nguyen Thi Thuy Linh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Susceptibility Modeling

scholarly journals Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1576 ◽  
Author(s):  
Li Zhu ◽  
Lianghao Huang ◽  
Linyu Fan ◽  
Jinsong Huang ◽  
Faming Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Environmental Factors ◽  
Recurrent Neural Network ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Rate

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.

scholarly journals Landslide Susceptibility Assessment Using an AutoML Framework

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.

scholarly journals Bagging-based machine learning algorithms for landslide susceptibility modeling

2021 ◽  
Author(s):  
Tingyu Zhang ◽  
Quan Fu ◽  
Hao Wang ◽  
Fangfang Liu ◽  
Huanyuan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Susceptibility Modeling
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