Tunnel geothermal disaster susceptibility evaluation based on interpretable ensemble learning: A case study in Ya'an–Changdu section of the Sichuan–Tibet traffic corridor
暂无分享,去创建一个
Huadong Guo | Ruichun Chang | Zhengbo Yu | Quanping Zhang | Yu Chen | X. Pei | Zhe Chen | Ziqiong He | Wenbo Zhao
[1] P. Cui,et al. Vulnerability assessment for buildings exposed to torrential hazards at Sichuan-Tibet transportation corridor , 2022, Engineering Geology.
[2] P. Cui,et al. Spatial and temporal distribution of landslide-dammed lakes in Purlung Tsangpo , 2022, Engineering Geology.
[3] P. Shen,et al. Initiation mechanisms and dynamics of a debris flow originated from debris-ice mixture slope failure in southeast Tibet, China , 2022, Engineering Geology.
[4] M. He,et al. Excavation compensation method and key technology for surrounding rock control , 2022, Engineering Geology.
[5] Huadong Guo,et al. Prediction of Potential Geothermal Disaster Areas along the Yunnan-Tibet Railway Project , 2022, Remote. Sens..
[6] Álvaro Moreno-Martínez,et al. Learning main drivers of crop progress and failure in Europe with interpretable machine learning , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[7] Zhenyu Sun,et al. Scientific problems and research proposals for Sichuan-Tibet railway tunnel construction , 2021, Underground Space.
[8] Yanbo Zhang,et al. Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP , 2021, Comput. Biol. Medicine.
[9] Jianping Chen,et al. Weighted Information Models for the Quantitative Prediction and Evaluation of the Geothermal Anomaly Area in the Plateau: A Case Study of the Sichuan-Tibet Railway , 2021, Remote. Sens..
[10] Shucai Li,et al. China starts the world’s hardest “Sky-High Road” project: Challenges and countermeasures for Sichuan-Tibet railway , 2021, Innovation.
[11] Omid Ghorbanzadeh,et al. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran , 2021, Geoscience Frontiers.
[12] Songjukta Datta,et al. Applying interpretable machine learning to classify tree and utility pole related crash injury types , 2021, IATSS Research.
[13] Jiejin Cai,et al. An assembly-level neutronic calculation method based on LightGBM algorithm , 2021 .
[14] Wei Chen,et al. Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer , 2021, Geoscience Frontiers.
[15] Shuai Zhang,et al. Study on the major minerals potential in China , 2020 .
[16] Yaning Liu,et al. Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net. , 2020, Analytical biochemistry.
[17] Rahmat Budiarto,et al. Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking , 2020, IEEE Access.
[18] Dieu Tien Bui,et al. A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers , 2019, Geocarto International.
[19] Zhiwen Yu,et al. A survey on ensemble learning , 2019, Frontiers of Computer Science.
[20] Chunfang Lu,et al. Challenges and Countermeasures for Construction Safety during the Sichuan–Tibet Railway Project , 2019, Engineering.
[21] Ivan Marchesini,et al. A global slope unit-based method for the near real-time prediction of earthquake-induced landslides , 2019, Geomorphology.
[22] John W. Paisley,et al. Global Explanations of Neural Networks: Mapping the Landscape of Predictions , 2019, AIES.
[23] Andino Maseleno,et al. Optimal feature-based multi-kernel SVM approach for thyroid disease classification , 2018, The Journal of Supercomputing.
[24] Tri Dev Acharya,et al. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .
[25] Jun Ren,et al. Research Survey on Support Vector Machine , 2017 .
[26] Tie-Yan Liu,et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.
[27] Eesha Goel,et al. Random Forest: A Review , 2017 .
[28] Sunil Kumar,et al. Field validation of an invasive species Maxent model , 2016, Ecol. Informatics.
[29] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[30] Z. Duan,et al. Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite , 2015 .
[31] Qiao Ji-hon. Geothermal Characteristics of Groundwater Seeping Division and Geothermal Hazard Prevention of Tunnel , 2015 .
[32] Xiao Qiu-gou. Thermal Regime and Paleogeothermal Gradient Evolution of Mesozoic-Cenozoic Sedimentary Basins in the Tibetan Plateau,China , 2013 .
[33] Y. Bayrak,et al. Regional variations and correlations of Gutenberg-Richter parameters and fractal dimension for the different seismogenic zones in Western Anatolia , 2012 .
[34] D. Aydoğan. Extraction of lineaments from gravity anomaly maps using the gradient calculation: Application to Central Anatolia , 2011 .
[35] Chris W. Clegg,et al. Opening the black box: A multi-method analysis of an enterprise resource planning implementation , 2009, J. Inf. Technol..
[36] Wang Zhi-yi,et al. GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT USING ANALYTICAL HIERARCHY PROCESS IN WENCHUAN EARTHQUAKE REGION , 2009 .
[37] Miquel Ninyerola,et al. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[38] S. Manel,et al. Evaluating presence-absence models in ecology: the need to account for prevalence , 2001 .
[39] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[40] J A Swets,et al. Measuring the accuracy of diagnostic systems. , 1988, Science.