The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China
Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content...
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/4/372 |
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| author | Zhongjun Ma Shengwu Qin Chen Cao Jiangfeng Lv Guangjie Li Shuangshuang Qiao Xiuyu Hu |
| author_facet | Zhongjun Ma Shengwu Qin Chen Cao Jiangfeng Lv Guangjie Li Shuangshuang Qiao Xiuyu Hu |
| author_sort | Zhongjun Ma |
| collection | DOAJ |
| description | Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping. |
| first_indexed | 2024-04-11T12:36:33Z |
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| spelling | doaj.art-3ee730acab6e4275bc591603ac9d22f82022-12-22T04:23:36ZengMDPI AGEntropy1099-43002019-04-0121437210.3390/e21040372e21040372The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast ChinaZhongjun Ma0Shengwu Qin1Chen Cao2Jiangfeng Lv3Guangjie Li4Shuangshuang Qiao5Xiuyu Hu6College of Construction Engineering, Jilin University, Changchun 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun 130026, ChinaLandslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.https://www.mdpi.com/1099-4300/21/4/372landslide susceptibility mappingChangbai Mountain arearough setAHPentropy weight methodCohen’s kappa indexGIS |
| spellingShingle | Zhongjun Ma Shengwu Qin Chen Cao Jiangfeng Lv Guangjie Li Shuangshuang Qiao Xiuyu Hu The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China Entropy landslide susceptibility mapping Changbai Mountain area rough set AHP entropy weight method Cohen’s kappa index GIS |
| title | The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China |
| title_full | The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China |
| title_fullStr | The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China |
| title_full_unstemmed | The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China |
| title_short | The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China |
| title_sort | influence of different knowledge driven methods on landslide susceptibility mapping a case study in the changbai mountain area northeast china |
| topic | landslide susceptibility mapping Changbai Mountain area rough set AHP entropy weight method Cohen’s kappa index GIS |
| url | https://www.mdpi.com/1099-4300/21/4/372 |
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