Analysis of prediction algorithm for forest land spatial evolution trend in rural planning
文献类型：期刊论文
| 作者 | Jiang, Xiujuan1,2; Zhang, Nan1; Huang, Jinchuan3 ; Zhang, Ping4; Liu, Hui1,5
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| 刊名 | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
 |
| 出版日期 | 2021-01-18 |
| 页码 | 9 |
| 关键词 | Rural planning Forest Evolve Data mining Support vector machine |
| ISSN号 | 1386-7857 |
| DOI | 10.1007/s10586-020-03227-7 |
| 通讯作者 | Jiang, Xiujuan(414647852@qq.com) |
| 英文摘要 | This study is to find the factors that affect the spatial change of forest land and purposefully predict the evolution trend of forest land space, so as to facilitate the rural planning work. The rural forest land situation in Zhangjiakou City of Hebei Province is analyzed, and the future evolution and development of forest space are predicted through analysis of correlation between the forest land influencing factors and the forest land productivity. Meanwhile, the multiple linear regression (MLR) prediction algorithm and support vector machine (SVM) are compared to obtain a more accurate prediction algorithm, which provides a strong basis for rural planning. The research results show that the annual rainfall and rainfall erosion have poor correlation with the spatial evolution of forest land relatively; while the average annual temperature is negatively correlated with annual rainfall and the rainfall erosivity. In addition, the soil erosion and terrain undulation of forest land have higher correlations with the rainfall erosivity due to abundant rainfall. The steeper the slope, the less human interference. What's more, the prediction value of SVM is closer to the actual value with smaller absolute error, so it is more accurate than MLR. Therefore, research on the prediction algorithm provides new ideas for enriching the prediction algorithms of the spatial evolution trend, and is of great significance for improving the forest resource reserve capacity and meeting more forest resource demand in China. In addition, it can optimize the natural environmental quality, so it can be applied to rural planning and construction. |
| 资助项目 | Chinese Academy of Sciences[XDA19040501] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000608663200001 |
| 出版者 | SPRINGER |
| 资助机构 | Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/136817]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Jiang, Xiujuan |
| 作者单位 | 1.Cent South Univ, Sch Architecture & Art, Changsha 410082, Hunan, Peoples R China 2.Zhengzhou Univ Aeronaut, Sch Civil Engn & Architecture, Zhengzhou 450051, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Hunan Univ Sci & Technol, Sch Architecture & Art Design, Xiangtan 411100, Peoples R China 5.Hunan Inst Sci & Technol, Coll Civil Engn & Architecture, Yueyang 414000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Xiujuan,Zhang, Nan,Huang, Jinchuan,et al. Analysis of prediction algorithm for forest land spatial evolution trend in rural planning[J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,2021:9. |
| APA | Jiang, Xiujuan,Zhang, Nan,Huang, Jinchuan,Zhang, Ping,&Liu, Hui.(2021).Analysis of prediction algorithm for forest land spatial evolution trend in rural planning.CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,9. |
| MLA | Jiang, Xiujuan,et al."Analysis of prediction algorithm for forest land spatial evolution trend in rural planning".CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2021):9. |
入库方式： OAI收割
来源：地理科学与资源研究所
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