An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China

Deliang Sun; Jiahui Xu; Haijia Wen; Yue Wang

doi:10.1007/s12583-020-1072-9

Volume 31 Issue 6

Dec 2020

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Journal of Earth Science > 2020 > 31(6): 1068-1086.

Deliang Sun, Jiahui Xu, Haijia Wen, Yue Wang. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China. Journal of Earth Science, 2020, 31(6): 1068-1086. doi: 10.1007/s12583-020-1072-9

Citation:

Deliang Sun, Jiahui Xu, Haijia Wen, Yue Wang. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China. Journal of Earth Science, 2020, 31(6): 1068-1086. doi: 10.1007/s12583-020-1072-9

Citation:

Deliang Sun, Jiahui Xu, Haijia Wen, Yue Wang. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China. Journal of Earth Science, 2020, 31(6): 1068-1086. doi: 10.1007/s12583-020-1072-9

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An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China

doi: 10.1007/s12583-020-1072-9

Deliang Sun^1
,,
Jiahui Xu¹,
Haijia Wen^{2, 3, 4
,
,
,},
Yue Wang¹

1.
Key Laboratory of GIS Application Research, Chongqing Normal University, Chongqing 401331, China
2.
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400045, China
3.
National Joint Engineering Research Centre of Geohazards Prevention in the Reservoir Areas, Chongqing 400044, China
4.
School of Civil Engineering, Chongqing University, Chongqing 400045, China

More Information

Corresponding author: Haijia Wen, jhw@cqu.edu.cn
Received Date: 20 May 2020
Accepted Date: 27 Aug 2020
Publish Date: 18 Dec 2020

Abstract

Abstract

Numerous researches have been published on the application of landslide susceptibility assessment models; however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored. This study is purposed to develop an optimized random forest (RF) model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping (LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model. Two typical counties (Fengjie County and Wushan County) in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example. To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1 522 landslides were then identified in Fengjie County. 22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared. Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses. An optimized RF model (Fengjie-based model) with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed. Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic (ROC) curve value (AUC) were used to estimate the accuracy. The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated. The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjie-based model had a great generalization ability in area other than the origin of the model. The Fengjie-based model in this study could be applied in other similar areas/countries with the same terrain and geological conditions,and a LSM may be generated without collecting landslide information for modeling,so as to reduce workload and improve efficiency in practice.
- landslide susceptibility mapping,
- generalization ability,
- random forest,
- Three Gorges Reservoir area,
- 10-fold cross-validation

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References(52)

References

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An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China

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An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir, China

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