Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an–Changdu section of the Sichuan–Tibet traffic corridor

• Published in: Engineering geology 2023-02, Vol.313, p.106985, Article 106985
• Main Authors: Chen, Zhe, Chang, Ruichun, Pei, Xiangjun, Yu, Zhengbo, Guo, Huadong, He, Ziqiong, Zhao, Wenbo, Zhang, Quanping, Chen, Yu
• Format: Article
• Language: English
• Subjects: Ensemble learning; Interpretability; Sichuan–Tibet traffic corridor; Susceptibility evaluation; Tunnel geothermal disasters
• ISSN: 0013-7952; 1872-6917
• DOI: 10.1016/j.enggeo.2023.106985

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 susceptibility evaluations. The main goal was to provide more scientific theoretical support for the accurate evaluation 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. •An interpretable model is proposed by combining ensemble learning with three interpretation algorithms.•The main factors leading to the occurrence of tunnel geothermal disasters are analysed.•Three key areas susceptible to tunnel geothermal di