Journal ArticleDOI
A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network
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In this article, a landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map, and a total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve.Abstract:
For the purpose of comparing susceptibility mapping methods in Mizunami City, Japan, the landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map. A total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve (AUC). The validation results indicated that the logistic regression model could provide the highest AUC value (0.865), and a relatively high percentage of landslide points fell in the high and very high landslide susceptibility classes in this study. Furthermore, the paper also suggested that the model performances would be increased if appropriate landslide points were used for the calculation.read more
Citations
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Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)
TL;DR: The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms and had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area.
Journal ArticleDOI
Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China.
TL;DR: The experimental results demonstrated that the proportions of highly susceptible zones in all of the CNN landslide susceptibility maps are highly similar and lower than 30%, which indicates that these CNNs are more practical for landslide prevention and management than conventional methods.
Journal ArticleDOI
Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
TL;DR: In this article, three well-known machine learning models namely maximum entropy (MaxEnt), support vector machine (SVM), and Artificial Neural Network (ANN) were used accompanied by their ensembles in Wanyuan area, China.
Journal ArticleDOI
Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling
TL;DR: GIS-based new ensemble data mining techniques that involve an adaptive neuro-fuzzy inference system (ANGIS) with genetic algorithm, differential evolution, and particle swarm optimization for landslide spatial modelling and its zonation can be applied for land use planning and management of landslide susceptibility and hazard in the study area and in other areas.
Journal ArticleDOI
Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
Binh Thai Pham,Indra Prakash,Sushant K. Singh,Ataollah Shirzadi,Himan Shahabi,Thi-Thu-Trang Tran,Dieu Tien Bui +6 more
TL;DR: Hybrid machine learning approaches of Reduced Error Pruning Trees (REPT) and different ensemble techniques were used for the construction of four novel hybrid models namely Bagging based Reduced error Pruning trees (BREPT), MultiBoost based Reducederror Pruning Tree (MBREPT), Rotation Forest-based reduced Error Pruned Trees (RFREPT%), and Random Subspace-based Reduced Error Trees (RSREPT).
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The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan
TL;DR: In this paper, a landslide susceptibility map in the Kakuda-Yahiko Mountains of Central Japan is presented, where the authors use logistic regression to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of independent parameters such as slope angle and lithology.
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A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
TL;DR: In this paper, three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were compared for landslide susceptibility mapping at Penang Hill area, Malaysia.
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Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling
Biswajeet Pradhan,Saro Lee +1 more
TL;DR: The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method.