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
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TLDR
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.Abstract:
The main purpose of the present study is to use 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. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.read more
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Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: A brief overview of text classification algorithms is discussed in this article, where different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods are discussed, and the limitations of each technique and their application in real-world problems are discussed.
Journal ArticleDOI
Text Classification Algorithms: A Survey
Kamran Kowsari,Kiana Jafari Meimandi,Mojtaba Heidarysafa,Sanjana Mendu,Laura E. Barnes,Donald E. Brown +5 more
TL;DR: An overview of text classification algorithms is discussed, which covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods.
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
Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
Haoyuan Hong,Haoyuan Hong,Junzhi Liu,Junzhi Liu,Dieu Tien Bui,Biswajeet Pradhan,Biswajeet Pradhan,Tri Dev Acharya,Binh Thai Pham,A-Xing Zhu,A-Xing Zhu,Wei Chen,Baharin Bin Ahmad +12 more
TL;DR: Wang et al. as mentioned in this paper investigated and compared the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT).
Journal ArticleDOI
Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling
TL;DR: The main aim of the present study is to explore and compare three state-of-the art data mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide susceptibility assessment in the Longhai area of China.
References
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