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Long tunnel group driving fatigue detection model based on xgboost algorithm

Journal of Traffic and Transportation Engineering(English Edition) 2025年第1期12卷 167-179页

作者： Huazhi Yuan Kun Zhao Ying Yan Li Wan Zhending Tian Xinqiang Chen School of Civil Engineering Lanzhou University of TechnologyLanzhou 730050China School of Transportation Engineering Chang'an UniversityXi'an 710064China Shandong Transportation Planning and Design Institute Group Co. Ltd.Jinan 250031China China Railway First Survey and Design Institute Group Co. Ltd.Xi'an 710043China Institute of Logistics Science and Engineering Shanghai Maritime UniversityShanghai 201306China

Driving fatigue is one of the important causes of accidents in tunnel(group)*** this paper,in order to effectively identify the driving fatigue of tunnel(group)drivers,an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel(group)expressways and thus obtain the eye movement,driving duration,and Karolinska sleepiness scale(KSS)data of 30 *** impacts of the tunnel and non-tunnel sections on drivers were compared,and the relationship between blink indexes,such as the blink frequency,blink duration,mean value of blink duration,driving duration,and driving fatigue,was studied.A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue.A driving fatigue detection model was then developed based on the xgboost *** obtained results show that the blink frequency,total blink duration,and mean value of blink duration gradually increase with the deepening of driving fatigue,and the mean value of blink duration is the most sensitive in the tunnel *** addition,a significant correlation exists between the driving duration index and driving fatigue,which can provide a reference for improving the tunnel *** the mean value of blink duration and driving duration as the characteristic indexes,the accuracy of the driving fatigue detection model based on the xgboost algorithm reaches 98%.The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel(group)environment.

关键词： Traffic safety Tunnel group Driving fatigue detection Eye movement data Driving duration xgboost algorithm

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Machine learning-based assessment of thermal comfort for the elderly in warm environments: Combining the xgboost algorithm and human body exergy analysis

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 209卷

作者： He, Mengyuan Liu, Hong Zhou, Shan Yao, Yan Kosonen, Risto Wu, Yuxin Li, Baizhan Chongqing Univ Joint Int Res Lab Green Bldg & Built Environm Minist Educ Chongqing Peoples R China Chongqing Univ Natl Ctr Int Res Low carbon & Green Bldg Minist Sci & Technol Chongqing Peoples R China Aalto Univ Dept Mech Engn Espoo 02150 Finland Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou Peoples R China

Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 degrees C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and xgboost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1-0.5 scores and 0.4-0.5 degrees C by the behavioural adaptation of fan cooling. The predictive results showed that the xgboost model performed better, with R2 score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (Ex-Esk), mean skin temperature (mtsk), air velocity (va), and convective exergy transfer (Ex-C) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.

关键词： Elderly Human body exergy analysis Machine learning Thermal comfort xgboost algorithm

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Evaluation and Application of the PT-JPL Physical Model Optimized with xgboost algorithm in Latent Heat Flux Estimation

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WATER RESOURCES MANAGEMENT 2025年 1-18页

作者： Wang, Lizheng Kong, Jinling Zhang, Qiutong Dong, Lixin Zhong, Yanling Changan Univ Sch Geol Engn & Geomat Xian 710054 Peoples R China China Meteorol Adm Natl Satellite Meteorol Ctr Natl Ctr Space Weather Key Lab Radiometr Calibrat & Validat Environm Sate Beijing Peoples R China

The static parameterization scheme in the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model limits the dynamic capture of latent heat flux (LE) in different plant functional types (PFTs). Therefore, this study employs the Extreme Gradient Boosting (xgboost) algorithm to optimize the constraint factors and sub-models in the PT-JPL model that are influenced by sensitive prior parameters, thereby constructing five hybrid models under the PT-JPL physical constraint framework to achieve the dynamic response of fixed prior parameters and to integrate ensemble learning (EML) with the process-based framework, ensuring that physical mechanism and high precision coexist. Comparative analysis and validation across five PFTs in the Heihe River Basin of China reveal that the XGB-LEc-PT-JPL model, optimized for vegetation transpiration, exhibits the best comprehensive performance and outperforms the pure data-driven model in several aspects. Regarding overall accuracy, the MAE and RMSE are 15.47 W/m(2) and 23.85 W/m(2), respectively. Although hybrid models optimized for deeper constraint factors sometimes exceed the simulation accuracy of the XGB-LEc-PT-JPL model, they often exhibit reduced parameter generalization, increasing model uncertainty. Finally, the regional scale comparison of different models reveals a consistent spatial pattern, and the XGB-LEc-PT-JPL model can still achieve good simulation accuracy. This study combines EML with physical model, providing scientific insights for understanding hydrological processes under regional climate change, as well as for ecological water resource conservation and optimal water resource allocation.

关键词： Evapotranspiration Combining ensemble learning algorithm with physical model PT-JPL model Physical constraints xgboost algorithm

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A multi-dimensional decision framework based on the xgboost algorithm and the constrained parametric approach

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Wang, Xinxin Zhang, Bingbing Xu, Zeshui Li, Ming Skare, Marinko Sichuan Univ Business Sch Chengdu 610064 Peoples R China Juraj Dobrila Univ Pula Fac Econ & Tourism Dr Mijo Mirkov Preradoviceva 1-1 Pula 52100 Croatia Univ Econ & Human Sci Warsaw Warsaw Poland

With the wide access to data and advanced technologies, organizations and firms prefer to use data-based and interpretable analytics to deal with uncertain and cognitive decision-making problems. In this regard, this study considers quantitative data and qualitative variables, to propose a multi-dimensional decision framework based on the nested probabilistic linguistic term sets. Under the framework, xgboost algorithm, one of the machine learning methods, is conducted to capture the importance of attributes by using the historical data, and further calculate the attribute weights. The constrained parametric approach is used to establish membership functions of linguistic variables, and then get the objective probabilities in the linguistic model, so that we can obtain a scientific decision matrix. A case study concerning the ranking of bank credit is applied to present the proposed decision framework, and the process of making a rational decision. According to the comparative analysis, the proposed framework is flexible and the result is stable. Managers and policymakers determine the attribute weights by real data and choose the suitable decision method for a certain application. The framework provides an opportunity for capturing, integrating, analyzing data, and interpreting linguistic variables in the model to consider uncertain and cognitive decision at the both theoretical and practical levels.

关键词： Multi-dimensional decision xgboost algorithm Nested probabilistic linguistic term sets Constrained parametric approach Bank credit

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PREDICTION AND OPTIMIZATION OF LIGHT ENVIRONMENT IN THE READING SPACE OF A LIBRARY BUILDING BASED ON xgboost algorithm AND MULTI-OBJECTIVE GENETIC algorithm

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JOURNAL OF GREEN BUILDING 2025年第2期20卷 327-349页

作者： Liang, Chunhang Shi, Xueying Yan, Hainan Henan Univ Sch Civil Engn & Architecture Kaifeng 475004 Peoples R China Nanjing Univ Sch Architecture & Urban Planning Nanjing 210093 Peoples R China

With the emergence of high-rise libraries in Chinese universities, improving the light environment of reading spaces is crucial for users. This study takes a university library in central China as an example to predict and optimize the light environment of the reading space inside the library. The results show that (1) the xgboost algorithm performs the best in the algorithm performance comparison, and its overall prediction accuracy for the light environment performance metrics is R-2 = 0.9653;(2) After optimization by a multi-objective genetic algorithm, the Useful Daylight Illuminance (UDI) value of the reading space was increased from the initial 72.52% to 85.54%, the Uniformity Ratio of Daylight (URD) value was increased from the initial 37.24% to 75.22%, and the spatial Glare Autonomy (sGA) value was increased from the initial 81.55% to 93.76%;(3) Interpretable machine learning analysis results show that south-north window-to-wall ratios of the exterior facades, east-west window-to-wall ratios of the exterior facades and sill heights for library facades have the most significant impact on the daylighting of the reading space. The research framework proposed in this paper can fully explore the potential of light environment optimization in library reading space and provide theoretical references for designers in the early stages of library building design.

关键词： Library Light Environment xgboost algorithm Multi-objective Optimization SHAP algorithm

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Integrated framework to integrate Spark-based big data analytics and for health monitoring and recommendation in sports using xgboost algorithm

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SOFT COMPUTING 2023年第2期28卷 1585-1608页

作者： Zhao, Yin Ramos, Ma. Finipina Li, Bin Southwest Med Univ Sch Phys Educ Studies Sect Luzhou 646000 Sichuan Peoples R China Jose Rizal Univ Jose Rizal Univ Grad Sch Mandaluyong 1552 Philippines Southwest Med Univ Sch Phys Educ Luzhou 646000 Sichuan Peoples R China

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the xgboost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the xgboost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, xgboost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the xgboost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores

关键词： Big data Spark Data mining xgboost algorithm Sports medical integration Service system construction

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Research on a Prediction Model Based on a Newton-Raphson-Optimization-xgboost algorithm Predicting Environmental Electromagnetic Effects for an Airborne Synthetic Aperture Radar

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ELECTRONICS 2025年第11期14卷 2202-2202页

作者： Shen, Yan Chen, Yazhou Wang, Yuming Ma, Liyun Zhang, Xiaolu Army Engn Univ PLA Shijiazhuang Campus Shijiazhuang 050003 Peoples R China

Airborne synthetic aperture radar (SAR) serves as critical battlefield reconnaissance equipment, yet it remains vulnerable to electromagnetic interference (EMI) in combat environments, leading to image-quality degradation. To address this challenge, this study proposes an EMI-effect prediction framework for airborne SAR electromagnetic environments, based on the Newton-Raphson-based optimization (NRBO) and xgboost algorithms. The methodology enables interference-level prediction through electromagnetic signal parameters obtained from reconnaissance operations, providing operational foundations with which SAR systems can mitigate the impacts of EMI. A laboratory-based airborne SAR EMI test system was developed to establish mapping relationships between EMI signal parameters and SAR imaging performance degradation. This experimental platform facilitated EMI-effect investigations across diverse interference scenarios. An evaluation methodology for SAR image degradation caused by EMI was formulated, revealing the characteristic influence patterns of different interference signals in the context of SAR imagery. The NRBO-xgboost framework was established through algorithmic integration of Newton-Raphson search principles with trap avoidance mechanisms from the Newton-Raphson optimization algorithm, optimizing the xgboost hyperparameters. Utilizing the developed test system, comprehensive EMI datasets were constructed under varied interference conditions. Comparative experiments demonstrated the NRBO-xgboost model's superior accuracy and generalization performance relative to conventional prediction approaches.

关键词： synthetic aperture radar (SAR) prediction of electromagnetic environmental effects NRBO algorithm xgboost algorithm

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Classification of earthquakes, explosions and mining-induced earthquakes based on xgboost algorithm

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COMPUTERS & GEOSCIENCES 2023年 170卷

作者： Wang, Tingting Bian, Yinju Zhang, Yixiao Hou, Xiaolin China Earthquake Adm Inst Geophys Beijing 100081 Peoples R China

The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established 36-dimensional and 201dimensional datasets by using feature extraction and amplitude spectral analysis. The Extreme Gradient Boosting (xgboost) supervised algorithm is introduced for the discrimination of couples-class and three-class. The accuracies in the earthquakes/explosions discrimination with feature extraction dataset and amplitude spectrum dataset are 97.48% and 95.12%, respectively, which shows that feature extraction can effectively quantify the differences between earthquakes and explosions. For the classification of earthquakes/mininginduced earthquakes and explosions/mining-induced earthquakes, the performance of xgboost with the amplitude spectrum dataset is greater, with accuracies of 99.24% and 95.33%, respectively. In the classification of the three types of events, the accuracies of xgboost are 96.41% for earthquakes, 90.38% for explosions, and 94.04% for mining-induced earthquakes. The performance indices of xgboost for different input parameters are invariably greater than those of the support vector machine (SVM), with stable classification ability, suggesting that the xgboost model has good prospects for application in seismic event classification.

关键词： Seismic events identification xgboost algorithm Feature extraction Model performance index

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A Hybrid ENSO Prediction System Based on the FIO-CPS and xgboost algorithm

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REMOTE SENSING 2023年第7期15卷 1728页

作者： Kuang, Zhiyuan Song, Yajuan Wu, Jie Fu, Qiuying Shu, Qi Qiao, Fangli Song, Zhenya Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China Minist Nat Resources Key Lab Marine Sci & Numer Modeling Qingdao 266061 Peoples R China Shandong Key Lab Marine Sci & Numer Modeling Qingdao 266061 Peoples R China Natl Climate Ctr China Meteorol Adm Key Lab Climate Predict Studies Beijing 100081 Peoples R China

Accurate prediction of the El Nino-Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly;however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (xgboost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO-CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO-CPS-HY. The latest 15 years (2007-2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Nino3.4 index from FIO-CPS v2.0 to FIO-CPS-HY for 7- to 13-month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 degrees C to 0.65 degrees C), respectively. The real-time predictions from FIO-CPS-HY indicated that the sea surface state of the Nino3.4 area would likely be in neutral conditions in 2023. Although FIO-CPS-HY still has some biases in real-time prediction, this study provides possible ideas and methods to enhance short-term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications.

关键词： ENSO prediction FIO-CPS v2.0 machine learning hybrid prediction xgboost algorithm

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A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the xgboost algorithm

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 49-49页

作者： Chen, Rui Zhang, Shengfa Li, Jie Guo, Dongwei Zhang, Weijun Wang, Xiaoying Tian, Donghua Qu, Zhiyong Wang, Xiaohua Cangzhou Cent Hosp Refined Management Off Cangzhou Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Populat Heath Data Ctr Beijing Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China Beijing Normal Univ Sch Social Dev & Publ Policy Beijing Peoples R China

BackgroundThe incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients' *** total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1-7 days, 8-14 days and more than 14 days. After preprocessing the data and feature selection, the xgboost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the xgboost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature *** with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke

关键词： Ischemic stroke xgboost algorithm Length of hospital stay (LOS) Machine learning (ML) model

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