气候变化领域集成服务门户(CCPortal): Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an-Changdu section of the Sichuan-Tibet traffic corridor

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DOI	10.1016/j.enggeo.2023.106985
	Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an-Changdu section of the Sichuan-Tibet traffic corridor
	Chen, Zhe; Chang, Ruichun; Pei, Xiangjun; Yu, Zhengbo; Guo, Huadong; He, Ziqiong; Zhao, Wenbo; Zhang, Quanping; Chen, Yu
发表日期	2023
ISSN	0013-7952
EISSN	1872-6917
卷号	313
英文摘要	An interpretable model based on the efficient combination of ensemble learning and permutation importance (PI), partial dependence plots (PDP), and the local interpretable model-agnostic explanations (LIME) algorithm was proposed in this work to facilitate the global and local interpretation of tunnel geothermal disaster sus-ceptibility evaluations. The main goal was to provide more scientific theoretical support for the accurate eval-uation of tunnel geothermal disaster susceptible areas. By considering the Ya'an-Changdu section of the Sichuan-Tibet traffic corridor as the research area - based on Landsat-8 images and other collected spatial data around the causes and distribution of the geothermal disasters in the tunnel - eight different evaluation factors were selected. Random forest (RF) and light gradient boosting machine (LightGBM) algorithms were used as the primary learning unit. In addition, a support vector machine (SVM) was used as the secondary learning unit. Through the implementation of the stacking algorithm, the susceptibility of tunnel geothermal disasters was evaluated. Then, the precision was verified by six evaluation indexes, and the interpretability of the ensemble learning model was studied by using three interpretation algorithms: PI, PDP, and LIME. From the extracted outcomes, it was demonstrated that the stacking algorithm for ensemble learning had the best performance and the highest prediction precision. The number of geothermal sample points in the high-susceptibility tunnel geothermal disaster area and the extremely highly susceptible area accounts for 85.57% of the total geothermal sample points, whereas the area accounts for 7.21% of the total area of the region. According to the model interpretation, land surface temperature (LST), fault density, and earthquake peak acceleration are regarded as the most important factors in the tunnel geothermal disaster susceptibility evaluation. The tunnel geothermal disaster susceptibility evaluation model based on interpretable ensemble learning has high precision, and is of great importance for the practical significance of the project route selection, construction, and operation of the Ya'an-Changdu section of the Sichuan-Tibet traffic corridor.
关键词	Interpretability Ensemble learning Tunnel geothermal disasters Susceptibility evaluation Sichuan-Tibet traffic corridor
英文关键词	PREDICTION; MODELS; AREA; TREE
WOS研究方向	Engineering, Geological ; Geosciences, Multidisciplinary
WOS记录号	WOS:000925262500001
来源期刊	ENGINEERING GEOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282697
作者单位	Chengdu University of Technology; Chengdu University of Technology; Chengdu University of Technology; Chinese Academy of Sciences; China University of Geosciences - Beijing; China Geological Survey
推荐引用方式 GB/T 7714	Chen, Zhe,Chang, Ruichun,Pei, Xiangjun,et al. Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an-Changdu section of the Sichuan-Tibet traffic corridor[J],2023,313.
APA	Chen, Zhe.,Chang, Ruichun.,Pei, Xiangjun.,Yu, Zhengbo.,Guo, Huadong.,...&Chen, Yu.(2023).Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an-Changdu section of the Sichuan-Tibet traffic corridor.ENGINEERING GEOLOGY,313.
MLA	Chen, Zhe,et al."Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an-Changdu section of the Sichuan-Tibet traffic corridor".ENGINEERING GEOLOGY 313(2023).