Evaluating and Predicting Groundwater Drought in Semi-Arid Plains by an Interpretable Machine Learning Model Optimized by the Sparrow Search Algorithm DOI
Zhiyuan Gan, Xianjun Xie, Chunli Su

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Machine learning-based prediction of outdoor thermal comfort: Combining Bayesian optimization and the SHAP model DOI
Ruiqi Guo, Bin Yang, Yuyao Guo

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 254, P. 111301 - 111301

Published: Feb. 22, 2024

Language: Английский

Citations

34
The impact of synergistic development of renewable energy and digital economy on energy intensity: Evidence from 33 countries DOI

Jianling Jiao,

Jiangfeng Song, Tao Ding

et al.

Energy, Journal Year: 2024, Volume and Issue: 295, P. 130997 - 130997

Published: March 13, 2024

Language: Английский

Citations

32
Understanding the relationship between 2D/3D variables and land surface temperature in plain and mountainous cities: Relative importance and interaction effects DOI

Pinyang Luo,

Bingjie Yu,

Pengfei Li

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 245, P. 110959 - 110959

Published: Oct. 20, 2023

Language: Английский

Citations

30
Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning DOI

Liudi Zhu,

Tingwei Cui,

A Runa

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 211, P. 262 - 280

Published: April 17, 2024

Language: Английский

Citations

13
Machine learning based thermal comfort prediction in office spaces: Integrating SMOTE and SHAP methods DOI
Yiliang Li, Feng Gao, Jiayue Yu

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115267 - 115267

Published: Jan. 1, 2025

Language: Английский

Citations

1
Physiological-signal-based prediction of occupant thermal comfort in a nonuniform transient vehicular cabin during winter DOI
Gineesh Gopi, Da Young Ju, Jung Kyung Kim

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112561 - 112561

Published: Jan. 1, 2025

Language: Английский

Citations

1
Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning DOI Creative Commons
Xin Chen, Hai Zhao,

Beini Wang

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(6), P. 865 - 865

Published: March 10, 2025

As global climate change intensifies, the frequency and severity of extreme weather events continue to rise. However, research on semi-outdoor transitional spaces remains limited, transportation stations are typically not fully enclosed. Therefore, it is crucial gain a deeper understanding environmental needs users in these spaces. This study employs machine learning (ML) algorithms SHAP (SHapley Additive exPlanations) methodology identify rank critical factors influencing outdoor thermal comfort at tram stations. We collected microclimatic data from Guangzhou, along with passenger feedback, construct comprehensive dataset encompassing parameters, individual perceptions, design characteristics. A variety ML models, including Extreme Gradient Boosting (XGB), Light Machine (LightGBM), Categorical (CatBoost), Random Forest (RF), K-Nearest Neighbors (KNNs), were trained validated, analysis facilitating ranking significant factors. The results indicate that LightGBM CatBoost models performed exceptionally well, identifying key determinants such as relative humidity (RH), air temperature (Ta), mean radiant (Tmrt), clothing insulation (Clo), gender, age, body mass index (BMI), location space occupied past 20 min prior waiting (SOP20). Notably, significance physical parameters surpassed physiological behavioral provides clear strategic guidance for urban planners, public transport managers, designers enhance while offering data-driven approach optimizing promoting sustainable development.

Language: Английский

Citations

1
Optimizing personal comfort: Short-term personalized heating impact on sanitation workers' thermo-physiological responses DOI

Chujian Gu,

Yang Li,

Shi Chen

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 112112 - 112112

Published: Sept. 1, 2024

Language: Английский

Citations

8
Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning DOI Creative Commons
Ming Lei, Yuandong Wang, Guangxu Liu

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102887 - 102887

Published: Nov. 9, 2024

Language: Английский

Citations

7
An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings DOI
Zenghui Liu,

Yingnan Zhuang

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 95, P. 110080 - 110080

Published: July 4, 2024

Language: Английский

Citations

6