Haolei Zheng
Biographic Data
| ID | 7747731 |
|---|---|
| NAME | Haolei Zheng |
| GIVEN NAMES | Haolei |
| FAMILY NAME | Zheng |
| SIGNATURE | ZHENG H |
| AFFILIATIONS | Yunnan Normal University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Quantitative assessment of building losses in China’s coastal regions due to sea-level rise under extreme climate conditions
Against the backdrop of global climate change, the superposition of extreme sea level events and long-term sea-level rise poses a severe threat to coastal regions. However, there is currently a lack of large-scale and detailed quantitative assessments for building losses caused by sea-level rise. This study takes 14 coastal provinces (autonomous regions, municipalities, and special administrative regions) in China as the research area. By integra…
Machine learning identification of influencing factors of global Nation-Level hypertension prevalence
Hypertension remains a critical global public health challenge, with its complex etiology poorly captured by traditional linear models, especially regarding macro-level structural and gender-specific drivers. To address this, we employed an interpretable machine learning framework, combining XGBoost with SHAP and bootstrap resampling. This approach analyzed a global, nation-level panel dataset from 190 countries (1990-2019) across four macro dime…
No prominent works on this page.
Quantitative assessment of building losses in China’s coastal regions due to sea-level rise under extreme climate conditions
Against the backdrop of global climate change, the superposition of extreme sea level events and long-term sea-level rise poses a severe threat to coastal regions. However, there is currently a lack of large-scale and detailed quantitative assessments for building losses caused by sea-level rise. This study takes 14 coastal provinces (autonomous regions, municipalities, and special administrative regions) in China as the research area. By integra…
Machine learning identification of influencing factors of global Nation-Level hypertension prevalence
Hypertension remains a critical global public health challenge, with its complex etiology poorly captured by traditional linear models, especially regarding macro-level structural and gender-specific drivers. To address this, we employed an interpretable machine learning framework, combining XGBoost with SHAP and bootstrap resampling. This approach analyzed a global, nation-level panel dataset from 190 countries (1990-2019) across four macro dime…
Biostatistics (1 work) · Blood Pressure and Hypertension Studies (1 work) · Climate change (1 work) · Climate Change and Health Impacts (1 work) · Coastal hazards (1 work) · Epidemiology (1 work) · Flood Risk Assessment and Management (1 work) · Global health (1 work) · Global warming (1 work) · Health, Environment, Cognitive Aging (1 work)