Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Guo, Zhengfu; Shi, Yu; Huang, Faming; Fan, Xuanmei; Huang, Jingsong

doi:10.1016/j.gsf.2021.101249

Cited by 155 publications

(84 citation statements)

References 75 publications

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“…16a2,b2,c2,d2,e2). Overall, the silhouette coefficient of the precipitation clustering results under the historical and four SSPs are higher than 0.5, indicating that the clustering effects are acceptable (Guo et al 2021b;Jing et al 2021). The classification results of precipitation clusters are closely related to the spatial distribution of precipitation amount (Fig.…”

Section: Cluster Analysis Of Precipitationmentioning

confidence: 90%

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Jin

Chen

Zhong

et al. 2021

Theor Appl Climatol

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The Hanjiang River is the source of water for the Middle Route of the South-to-North Water Diversion Project. So a reasonable evaluation on water resources future changes in the Hanjiang River Basin (HRB) is essential to ensure its water safety. In this study, we firstly evaluated the China meteorological forcing dataset (CMFD), and then used it to measure the applicability of the eight global climate models (GCMs). Finally, the empirical orthogonal function (EOF) and clustering algorithms were used to analyze the spatial distribution status of precipitation in the HRB. The results showed that the BCC-CSM2-MR model has the best effect, followed by the IPSL-CM6A-LR model. In the SSP126, SSP245, SSP370, and SSP585 scenarios, total precipitation in the HRB all shows an increasing trend. However, the difference in the spatial distribution of precipitation intensified, for the upstream and downstream tended to be wetter, while the middle reaches had less precipitation. In the future climate scenarios, the areas with more precipitation (Class 1) increased significantly, while the areas with medium precipitation (Class 3) decreased significantly, which will enlarge the precipitation difference between regions in the HRB. The increase in the total amount of precipitation, coupled with the increase in the spatial heterogeneity of precipitation, will make the HRB more vulnerable to flooding disasters. Therefore, the water resources management measures in the HRB should be strengthened in the future.

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Section: Cluster Analysis Of Precipitationmentioning

confidence: 90%

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Jin

Chen

Zhong

et al. 2021

Theor Appl Climatol

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“…The calculated FR values of the corresponding environmental factors are set as the inputs of the machine learning. Finally, the trained machine learning is used to predict the CSIs of all grid cells in An'yuan County (Guo et al, 2021).…”

Section: Training and Validation Datasetsmentioning

confidence: 99%

Uncertainties of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Effects of Different Machine Learning Models

Li¹,

Shi²,

Huang³

et al. 2021

Front. Earth Sci.

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For the issue of collapse susceptibility prediction (CSP), minimal attention has been paid to explore the uncertainty characteristics of different machine learning models predicting collapse susceptibility. In this study, six kinds of typical machine learning methods, namely, logistic regression (LR), radial basis function neural network (RBF), multilayer perceptron (MLP), support vector machine (SVM), chi-square automatic interactive detection decision tree (CHAID), and random forest (RF) models, are constructed to do CSP. In this regard, An’yuan County in China, with a total of 108 collapses and 11 related environmental factors acquired through remote sensing and GIS technologies, is selected as a case study. The spatial dataset is first constructed, and then these machine learning models are used to implement CSP. Finally, the uncertainty characteristics of the CSP results are explored according to the accuracies, mean values, and standard deviations of the collapse susceptibility indexes (CSIs) and the Kendall synergy coefficient test. In addition, Huichang County, China, is used as another study case to avoid the uncertainty of different study areas. Results show that 1) overall, all six kinds of machine learning models reasonably and accurately predict the collapse susceptibility in An’yuan County; 2) the RF model has the highest prediction accuracy, followed by the CHAID, SVM, MLP, RBF, and LR models; and 3) the CSP results of these models are significantly different, with the mean value (0.2718) and average rank (2.72) of RF being smaller than those of the other five models, followed by the CHAID (0.3210 and 3.29), SVM (0.3268 and 3.48), MLP (0.3354 and 3.64), RBF (0.3449 and 3.81), and LR (0.3496 and 4.06), and with a Kendall synergy coefficient value of 0.062. Conclusively, it is necessary to adopt a series of different machine learning models to predict collapse susceptibility for cross-validation and comparison. Furthermore, the RF model has the highest prediction accuracy and the lowest uncertainty of the CSP results of the machine learning models.

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“…Based on the target definition, or rather, collection of samples for training, ML approaches can automatically analyze and extract rules from the input data to make predictions 14 . Meanwhile, it is highly efficient in calculating high-dimension data and can fit the nonlinear relationships between target and factors 8 , 29 – 31 . Nevertheless, the prediction accuracy of the most studies, even including those harnessing the hotspotted deep learning techniques 32 – 35 , comes between 75 and 85%, except for those of Huangfu et al 36 , Ou et al 26 , Zhang et al 27 and Zhou et al 28 , who have achieved landslide risk prediction with an accuracy of 86–94.54%.…”

Section: Introductionmentioning

confidence: 99%

Geoinformation-based landslide susceptibility mapping in subtropical area

Zhou

Qin

et al. 2021

Sci Rep

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Mapping susceptibility of landslide disaster is essential in subtropical area, where abundant rainfall may trigger landslide and mudflow, causing damages to human society. The purpose of this paper is to propose an integrated methodology to achieve such a mapping work with improved prediction results using hybrid modeling taking Chongren, Jiangxi as an example. The methodology is composed of the optimal discretization of the continuous geo-environmental factors based on entropy, weight of evidence (WoE) calculation and application of the known machine learning (ML) models, e.g., Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The results show the effectiveness of the proposed hybrid modeling for landslide hazard mapping in which the prediction accuracy vs the validation set reach 82.35–91.02% with an AUC [area under the receiver operating characteristic (ROC) curve] of 0.912–0.970. The RF algorithm performs best among the observed three ML algorithms and WoE-based RF modeling will be recommended for the similar landslide risk prediction elsewhere. We believe that our research can provide an operational reference for predicting the landslide hazard in the subtropical area and serve for disaster reduction and prevention action of the local governments.

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Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Cited by 155 publications

References 75 publications

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Uncertainties of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Effects of Different Machine Learning Models

Geoinformation-based landslide susceptibility mapping in subtropical area

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