人民长江2024，Vol.55Issue(1)：82-90,9.DOI:10.16232/j.cnki.1001-4179.2024.01.012
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
摘要
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分类
农业科学引用本文复制引用
李潼亮,李斌斌,张风宝,史方颖,杨明义,何庆..应用机器学习研究土壤侵蚀的文献计量分析[J].人民长江,2024,55(1):82-90,9.基金项目
国家自然科学基金项目(42077071,42177338) （42077071,42177338）
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) （SXLK2020-03-02）
