Yibo Geng
Biographic Data
| ID | 5978388 |
|---|---|
| NAME | Yibo Geng |
| GIVEN NAMES | Yibo |
| FAMILY NAME | Geng |
| SIGNATURE | GENG Y |
| AFFILIATIONS | Department of Land Resource Management, School of Public Administration Jiangxi University of Finance and Economics Nanchang China |
| ORCID | 0009-0007-3774-7529 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Mapping Soil Heavy Metal Using an Interpretable Framework With Multi‐Sources Data
Predicting the spatial heterogeneity of soil heavy metals (HMs) and identifying the determinants and sources is fundamental to developing effective strategies to mitigate and prevent environmental contamination. This study established a comprehensive research framework of “spatial prediction ‐ driver factor analysis ‐ source contribution quantification”. By comparing and using different methods to draw the spatial distribution map of HMs and comb…
Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model
High‐precision soil organic carbon density (SOCD) map is significant for understanding ecosystem carbon cycles and estimating soil organic carbon storage. However, the current mapping methods are difficult to balance accuracy and interpretability, which brings great challenges to the mapping of SOCD. In the present research, a total of 6223 soil samples were collected, along with data pertaining to 30 environmental covariates, from agricultural l…
No prominent works on this page.
Comparison of Machine Learning and Geostatistical Methods on Mapping Soil Organic Carbon Density in Regional Croplands and Visualizing Its Location‐Specific Dominators via Interpretable Model
High‐precision soil organic carbon density (SOCD) map is significant for understanding ecosystem carbon cycles and estimating soil organic carbon storage. However, the current mapping methods are difficult to balance accuracy and interpretability, which brings great challenges to the mapping of SOCD. In the present research, a total of 6223 soil samples were collected, along with data pertaining to 30 environmental covariates, from agricultural l…
Mapping Soil Heavy Metal Using an Interpretable Framework With Multi‐Sources Data
Predicting the spatial heterogeneity of soil heavy metals (HMs) and identifying the determinants and sources is fundamental to developing effective strategies to mitigate and prevent environmental contamination. This study established a comprehensive research framework of “spatial prediction ‐ driver factor analysis ‐ source contribution quantification”. By comparing and using different methods to draw the spatial distribution map of HMs and comb…
Geochemistry and Geologic Mapping (2 works) · Soil Geostatistics and Mapping (2 works) · Soil map (2 works) · Spatial variability (2 works) · Apportionment (1 work) · Artificial Intelligence (1 work) · Chemistry (1 work) · Computer Science (1 work) · Data mining (1 work) · Digital soil mapping (1 work)