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Your search keyword '"Chai, Huichan"' showing total 15 results
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15 results on '"Chai, Huichan"'

8. Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree

Author

Chen, Wei, primary, Zhao, Xia, additional, Shahabi, Himan, additional, Shirzadi, Ataollah, additional, Khosravi, Khabat, additional, Chai, Huichan, additional, Zhang, Shuai, additional, Zhang, Lingyu, additional, Ma, Jianquan, additional, Chen, Yingtao, additional, Wang, Xiaojing, additional, Bin Ahmad, Baharin, additional, and Li, Renwei, additional

Published
2019
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9. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

Author

He, Qingfeng, primary, Shahabi, Himan, additional, Shirzadi, Ataollah, additional, Li, Shaojun, additional, Chen, Wei, additional, Wang, Nianqin, additional, Chai, Huichan, additional, Bian, Huiyuan, additional, Ma, Jianquan, additional, Chen, Yingtao, additional, Wang, Xiaojing, additional, Chapi, Kamran, additional, and Ahmad, Baharin Bin, additional

Published
2019
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11. Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

Author

Chen, Wei, primary, Shahabi, Himan, additional, Zhang, Shuai, additional, Khosravi, Khabat, additional, Shirzadi, Ataollah, additional, Chapi, Kamran, additional, Pham, Binh, additional, Zhang, Tingyu, additional, Zhang, Lingyu, additional, Chai, Huichan, additional, Ma, Jianquan, additional, Chen, Yingtao, additional, Wang, Xiaojing, additional, Li, Renwei, additional, and Ahmad, Baharin, additional

Published
2018
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15. Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China.

Author

Chen, Wei, Li, Wenping, Hou, Enke, Bai, Hanying, Chai, Huichan, Wang, Danzhi, Cui, Xueli, and Wang, Qiqing

Abstract

The main goal of this study was to produce landslide susceptibility mapping by frequency ratio (FR), statistical index (SI), and index of entropy (IOE) models based on geographic information system (GIS) for the Baozhong region of Baoji, China. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out a field survey. A total of 79 landslides were mapped and out of which, 55 (70 %) were randomly selected for building landslide susceptibility models, while the remaining 24 (30 %) were used for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using FR, SI, and IOE models. Finally, the validation of landslide susceptibility map was carried out using areas under the curve (AUC). The AUC plot estimation results showed that the susceptibility map using FR model has the highest prediction accuracy of 82.49 %, followed by the SI model (81.43 %) and the IOE model (79.62 %). Similarly, the AUC plot showed that the success rate of the three models was 84.95 % for FR model, 82.37 % for SI model, and 82.05 % for IOE model, respectively. According to the validation results of the AUC evaluation, the map produced by the FR model exhibits the most satisfactory properties. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. [ABSTRACT FROM AUTHOR]

Published
2015
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