Journal ArticleDOI
Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea
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TLDR
In this article, the authors compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage.Abstract:
Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).read more
Citations
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Journal ArticleDOI
A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
Wei Chen,Xiaoshen Xie,Jiale Wang,Biswajeet Pradhan,Biswajeet Pradhan,Haoyuan Hong,Dieu Tien Bui,Zhao Duan,Jianquan Ma +8 more
TL;DR: In this article, the authors used three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility.
Journal ArticleDOI
Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
TL;DR: In this paper, support vector machine (SVM) is used to predict flood susceptibility in the Kuala Terengganu basin, Malaysia, and four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were used to check the robustness of the SVM model.
Journal ArticleDOI
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Omid Ghorbanzadeh,Thomas Blaschke,Khalil Gholamnia,Sansar Raj Meena,Dirk Tiede,Jagannath Aryal +5 more
TL;DR: The CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner, Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the results of augmentation strategies to artificially increase the number of existing samples are better understanding.
Journal ArticleDOI
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
Jie Dou,Ali P. Yunus,Dieu Tien Bui,Abdelaziz Merghadi,Mehebub Sahana,Zhongfan Zhu,Chi-Wen Chen,Khabat Khosravi,Yang Yang,Binh Thai Pham +9 more
TL;DR: It is suggested that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making.
Journal ArticleDOI
Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran
TL;DR: In this article, the authors investigated the application of the frequency ratio (FR) and weights-of-evidence (WofE) models for flood susceptibility mapping in the Golestan Province, Iran.
References
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Learning internal representations by error propagation
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
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Measuring the accuracy of diagnostic systems
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