应用机器学习研究土壤侵蚀的文献计量分析
Bibliometric analysis of soil erosion study by machine learning
李潼亮 1李斌斌 2张风宝 3史方颖 4杨明义 3何庆1
作者信息
- 1. 西北农林科技大学 水土保持科学与工程学院,陕西 杨凌 712100
- 2. 水利部水土保持监测中心,北京 100053
- 3. 西北农林科技大学 水土保持科学与工程学院,陕西 杨凌 712100;中国科学院水利部水土保持研究所,陕西 杨凌 712100
- 4. 中国科学院水利部水土保持研究所,陕西 杨凌 712100
- 折叠
摘要
为探究机器学习应用于土壤侵蚀领域的研究进展和发展趋势,基于CiteSpace等文献计量工具,借助Web of Science(WOS)核心合集数据库中收录的以机器学习应用于土壤侵蚀领域的相关文献,对该领域研究动态进行可视化展示与分类.结果表明:该领域研究成果不断增长,尤其2014 年后呈指数型增加;中国是该领域内发文量与被引量最多的国家,但中介中心性低于伊朗、美国;侵蚀敏感性分析是热点问题,大多数研究者目标是基于机器学习相较传统模型分析更快更精准的特点,开发高效侵蚀预测模型;深度学习和各类回归算法是广大研究者常用的方法.未来,研究者们应充分利用不同机器学习方法的特性,探索最新的深度学习预测性能,提高复杂环境条件下土壤侵蚀的预测预报精度,揭示主要影响因子的贡献及因子之间的相关作用机制.
Abstract
To explore the research progress and development trend of machine learning technology application in soil erosion field study,CiteSpace and other bibliometric tools were used to analyze the research progress,hotspots,author's cooperation net-work,and future research direction and development trend of machine learning technology in this field,based on the relevant docu-ments included in the Web of Science(WOS)core collection database.The results show that the research results in this field have been increasing exponentially since 2014.China has the largest number of publications and citations,but the intermediary centrali-ty is lower than that of Iran and the United States.Erosion sensitivity analysis is a hot issue in this field.Most of researchers devel-op efficient erosion prediction models based on the faster and more accurate characteristics of machine learning compared with tra-ditional models.Deep learning and various regression algorithms are the most commonly used machine learning methods.In the fu-ture,researchers should give full play to the characteristics of various types of machine learning,explore the latest prediction per-formance of deep learning,improve the prediction accuracy of soil erosion under complex environmental conditions,and reveal the contribution of main impact factors and the relevant mechanism between factors.
关键词
土壤侵蚀/机器学习/神经网络模型/地理信息系统/文献计量学Key words
soil erosion/machine learning/neural network model/GIS/bibliometrics引用本文复制引用
基金项目
国家自然科学基金(42077071)
国家自然科学基金(42177338)
陕西省林业科学院黄土高原生态修复创新团队项目(SXLK2020-03-02)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据