| Title |
Research on land resource management integrated with support vector machine -Based on the perspective of green innovation |
| ID_Doc |
31189 |
| Authors |
Jin, T; Liang, FY; Dong, XQ; Cao, XJ |
| Title |
Research on land resource management integrated with support vector machine -Based on the perspective of green innovation |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.resourpol.2023.104180 |
| Abstract |
Traditional methods of land resource management are no longer adequate to address the rapidly changing environmental and societal demands. By introducing data-driven approaches like SVM, this study offers a novel avenue for more precise analysis and prediction of land resource utilization trends, the green innovation serves to enhance the efficiency and ecological sustainability of land resource utilization through pioneering and technological means. there exists a close connection between land resource management, green innovation, and sustainable development. The experiment utilized a Support Vector Machine (SVM) as the foundational model and focused on land resources in Beijing, utilizing remote sensing imagery for prediction and planning. The experimental results demonstrate significant achievements in land resource classification prediction with the integrated SVM model, indicating a strong linear relationship between the prediction results and actual observation data. This suggests that the introduction of green innovation can enhance the effectiveness of land resource classification prediction, providing more efficient decision support for land resource management and sustainable development. These results offer important policy recommendations for the promotion of green space expansion and conservation policies, facilitation of data sharing and technology support policies, formulation of land resource management and integration of sustainable development goals. |
| Author Keywords |
Machine learning; Green innovation; Land resource management; Classification and prediction; Sustainable development |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:001100648000001 |
| WoS Category |
Environmental Studies |
| Research Area |
Environmental Sciences & Ecology |
| PDF |
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