[1]符敦凯,张云辉,徐小军,等.基于文献计量学的水质预测研究进展及趋势[J].华东地质,2024,45(01):88-100.[doi:10.16788/j.hddz.32-1865/P.2024.01.007]
　FU Dunkai,ZHANG Yunhui,XU Xiaojun,et al.Research progress and trend on water quality prediction based on bibliometric analysis[J].East China Geology,2024,45(01):88-100.[doi:10.16788/j.hddz.32-1865/P.2024.01.007]
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基于文献计量学的水质预测研究进展及趋势()
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《华东地质》[ISSN:2096-1871/CN:32-1865/P]

卷:
45
期数:
2024年01期
页码:
88-100
栏目:
第一届青年编委专辑(一)
出版日期:
2024-04-20

文章信息/Info

Title:
Research progress and trend on water quality prediction based on bibliometric analysis
作者:
符敦凯1; 2;  张云辉1; 2;  徐小军3;  王鹰1; 2;  许钟元1; 2;  王杨双1; 2
1. 西南交通大学地球科学与工程学院, 四川 成都 611756;
2. 宜宾西南交通大学研究院, 四川 宜宾 644000;
3. 四川省交通勘察设计研究院有限公司四川省交通运输内河港航工程技术研究中心, 四川 成都 610017
Author(s):
FU Dunkai1; 2;  ZHANG Yunhui1; 2;  XU Xiaojun3;  WANG Ying1; 2;  XU Zhongyuan1; 2;  WANG Yangshuang1; 2
1. Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, Sichuan, China;
2. Yibin Research Institute, Southwest Jiaotong University, Yibin 644000, Sichuan, China;
3. Sichuan Transportation Inland Waterway Harbor and Navigation Engineering Technology Research Center, Sichuan Communication Surveying and Design Institute CO., LTD, Chengdu 610017, Sichuan, China
关键词:
水质预测; 文献计量学; VOSviewer; CNKI数据库; WOS数据库
Keywords:
water quality prediction; bibliometrics; VOSviewer; CNKI database; WOS database
分类号:
X824
DOI:
10.16788/j.hddz.32-1865/P.2024.01.007
摘要:
随着社会经济的快速发展,我国各类水环境问题日益突出。水质预测研究基于大样本环境监测数据,对于提前制定水环境保护对策具有重要的支撑作用。但是,目前对水质预测的阶段性研究进展及趋势的总结分析还较少。文章基于文献计量学理论,对2000-2023年收录在中国知网(CNKI)中文文献数据库和WOS(web of science)核心合集文献库中的水质预测领域论文进行检索,采用VOSviewer软件对国内外相关文献进行综合分析,通过构建长时间的序列图谱,系统地梳理了该领域的研究进展与科研成果,揭示了关于水质预测领域的研究趋势。结果表明:水质预测研究是一个典型的多作者、多国家、多机构的合作领域;我国每年出版的水质预测论文数量最多,且科研成果一直处于世界领先地位,表明我国是水质预测研究领域的主导国家。通过分析关键词发现,与传统方法相比,BP神经网络以及深度学习等是近年来行之有效的水质预测方法。该研究将有助于提升我国水质预测的研究水准,为未来相关研究提供文献计量学成果参考。
Abstract:
With the development of social economy, various domestic water environment problems are gradually emerging. Water quality prediction based on large-sample environmental monitoring data plays a significant role in accurately formulating the countermeasure of environmental protection in advance, but there are fewer analytical studies related to the phasic summary of this subject. Based on the theory of bibliometrics, the article searches the papers in the field of water quality prediction included in the database of China Knowledge Network (CNKI) and WOS database from 2000 to 2023, and comprehensively overviews relevant domestic and foreign literature with VOSviewer software.By constructing a long time sequence mapping, the authors systematically comb the scientific research progress and achievements in the discipline, so as to exhibit the research status and trends of water quality prediction. The results show that water quality prediction research is a typical multi-author, multi-country, multi-institution cooperative field; China publishes the largest number of papers annually, and its scientific research outcomes has always been ranked in the top tier, indicating China’s global leading role in the research of water quality prediction. By analysing the keywords, it is found that compared with traditional way, BP neural networks and deep learning are effective methods of water quality prediction in recent years. This study will be conducive to improving the domestic research of water quality prediction and provide bibliometric references for future research.

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备注/Memo

备注/Memo:
收稿日期:2024-01-08;改回日期:2024-03-06。
基金项目:宜宾市科技项目"宜宾市主城区地下水环境时空演化及多元控制管理研究(编号:SWJTU2021020007)"、"宜宾市岩溶地质发育规律系统研究(编号:SWJTU2021020008)"和四川省交通运输科技"通航水域污染防治与应急处置技术研究(编号:2023-B-15)"项目联合资助。
第一作者简介:符敦凯,1997年生,男,硕士研究生,主要从事水环境研究工作。Email:fudk0722@163.com。
通信作者简介:张云辉,1990年生,男,副教授,博士,主要从事水文地质与地热地质教学科研工作。Email:zhangyunhui@swjtu.edu.cn。
更新日期/Last Update: 1900-01-01