IDEAS home Printed from https://ideas.repec.org/a/caa/jnlswr/v18y2023i2id94-2022-swr.html
   My bibliography  Save this article

Past, present and future of the applications of machine learning in soil science and hydrology

Author

Listed:
  • Xiangwei Wang

    (College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, P.R. China)

  • Yizhe Yang

    (Shaanxi Provincial Farmland Quality and Agricultural Environmental Protection Workstation, Department of Agriculture and Rural Affairs of Shaanxi Province, Xi'an, Shaanxi, P.R. China)

  • Jianglong Lv

    (College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, P.R. China
    Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Northwest A&F University, Yangling, P.R. China)

  • Hailong He

    (College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, P.R. China)

Abstract

Machine learning can handle an ever-increasing amount of data with the ability to learn models from the data. It has been widely used in a variety of disciplines and is gaining increasingly more attention nowadays. As it is challenging to map soil and hydrological information that are characterised with high spatial and temporal variability, applications of machine learning in soil science and hydrology (AMLSH) have become popularised. To better understand the current state of AMLSH research, a scientific and quantitative approach was performed to statistically analyse publication information from 1973 to 2021 archived in the Scopus database using scientometric analysis tools, including VOSviewer, CiteSpace, and the open-source R package "bibliometrix". The results show a significant increase in the number of publications on AMLSH since 2006. The major contributions were identified based on country origins (China, the USA, and India), institutions (Hohai University, Islamic Azad University, and Wuhan University), and journals (Journal of Hydrology, Remote Sensing, and Geoderma). The keywords analysis of the AMLSH research demonstrates four research hotspots: neural network, artificial intelligence, machine learning, and soil. The most frequently utilised machine learning (ML) methods are neural networks, decision trees, random forests and other methods for image processing and predictive analysis. McBratney et al. 2003 is the most highly cited article. Our research sheds light on the research process on AMLSH and concludes with future research perspectives.

Suggested Citation

  • Xiangwei Wang & Yizhe Yang & Jianglong Lv & Hailong He, 2023. "Past, present and future of the applications of machine learning in soil science and hydrology," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(2), pages 67-80.
    • Handle: RePEc:caa:jnlswr:v:18:y:2023:i:2:id:94-2022-swr
    DOI: 10.17221/94/2022-SWR
    as

    Download full text from publisher

    File URL: http://swr.agriculturejournals.cz/doi/10.17221/94/2022-SWR.html
    Download Restriction: free of charge
    File URL: http://swr.agriculturejournals.cz/doi/10.17221/94/2022-SWR.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/94/2022-SWR?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    3. Hossein Bonakdari & Isa Ebtehaj & Pijush Samui & Bahram Gharabaghi, 2019. "Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3965-3984, September.
    4. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    5. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    6. Hualin Xie & Yanwei Zhang & Zhilong Wu & Tiangui Lv, 2020. "A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions," Land, MDPI, vol. 9(1), pages 1-37, January.
    7. Lefeng Qiu & Kai Wang & Wenli Long & Ke Wang & Wei Hu & Gabriel S Amable, 2016. "A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression, Classification and Regression Tree, and Random Forest Models," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-16, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lang Jia & Wenjuan Wang & Francis Zvomuya & Hailong He, 2024. "Trends in Soil Science over the Past Three Decades (1992–2022) Based on the Scientometric Analysis of 39 Soil Science Journals," Agriculture, MDPI, vol. 14(3), pages 1-32, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qin, Yong & Xu, Zeshui & Wang, Xinxin & Škare, Marinko, 2022. "Green energy adoption and its determinants: A bibliometric analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    2. Samson Anjikwi Malgwi & Ropo Ebenezer Ogunsakin & Abolade David Oladepo & Matthew Adekunle Adeleke & Moses Okpeku, 2023. "A Forty-Year Analysis of the Literature on Babesia Infection (1982–2022): A Systematic Bibliometric Approach," IJERPH, MDPI, vol. 20(12), pages 1-19, June.
    3. Leng Liu & Congjie Cao & Wei Song, 2023. "Bibliometric Analysis in the Field of Rural Revitalization: Current Status, Progress, and Prospects," IJERPH, MDPI, vol. 20(1), pages 1-18, January.
    4. Yiming Xiao & Han Wu & Guohua Wang & Hong Mei, 2021. "Mapping the Worldwide Trends on Energy Poverty Research: A Bibliometric Analysis (1999–2019)," IJERPH, MDPI, vol. 18(4), pages 1-22, February.
    5. U Ubaidillah & Bhre Wangsa Lenggana & Seung-Bok Choi, 2022. "Bibliometric Review of Magnetorheological Materials," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    6. Khare, Apoorv & Jain, Rajesh, 2022. "Mapping the conceptual and intellectual structure of the consumer vulnerability field: A bibliometric analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 567-584.
    7. Wang, Xinxin & Qin, Yong & Xu, Zeshui & Škare, Marinko, 2022. "A look at the focus shift in innovation literature due to Covid-19 pandemic," Journal of Business Research, Elsevier, vol. 145(C), pages 1-20.
    8. Kun Shi & Yi Zhou & Zhen Zhang, 2021. "Mapping the Research Trends of Household Waste Recycling: A Bibliometric Analysis," Sustainability, MDPI, vol. 13(11), pages 1-23, May.
    9. Yao, Ye & Du, Huibin & Zou, Hongyang & Zhou, Peng & Antunes, Carlos Henggeler & Neumann, Anne & Yeh, Sonia, 2023. "Fifty years of Energy Policy: A bibliometric overview," Energy Policy, Elsevier, vol. 183(C).
    10. Javad Mohammadpour & Ann Lee & Victoria Timchenko & Robert Taylor, 2022. "Nano-Enhanced Phase Change Materials for Thermal Energy Storage: A Bibliometric Analysis," Energies, MDPI, vol. 15(9), pages 1-14, May.
    11. Qian Wang & Shixian Luo & Jiao Zhang & Katsunori Furuya, 2022. "Increased Attention to Smart Development in Rural Areas: A Scientometric Analysis of Smart Village Research," Land, MDPI, vol. 11(8), pages 1-28, August.
    12. Maria Lourdes Ordoñez Olivo & Zoltán Lakner, 2023. "Shaping the Knowledge Base of Bioeconomy Sectors Development in Latin American and Caribbean Countries: A Bibliometric Analysis," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    13. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    14. Juan F. Prados-Castillo & Miguel Ángel Solano-Sánchez & Pilar Guaita Fernández & José Manuel Guaita Martínez, 2023. "Potential of the Crypto Economy in Financial Management and Fundraising for Tourism," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    15. Ashraf, Rohail & Khan, Muhammad Asif & Khuhro, Rafique Ahmed & Bhatti, Zeeshan Ahmed, 2022. "Knowledge creation dynamics of technological forecasting and social change special issues," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    16. Shuangqing Sheng & Wei Song & Hua Lian & Lei Ning, 2022. "Review of Urban Land Management Based on Bibliometrics," Land, MDPI, vol. 11(11), pages 1-25, November.
    17. Trotta, Annarita & Rania, Francesco & Strano, Eugenia, 2024. "Exploring the linkages between FinTech and ESG: A bibliometric perspective," Research in International Business and Finance, Elsevier, vol. 69(C).
    18. Gour Gobinda Goswami & Tahmid Labib, 2022. "Modeling COVID-19 Transmission Dynamics: A Bibliometric Review," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    19. Alam Md Moshiul & Roslina Mohammad & Fariha Anjum Hira & Nurazean Maarop, 2022. "Alternative Marine Fuel Research Advances and Future Trends: A Bibliometric Knowledge Mapping Approach," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    20. Ying Liang & Wei Song, 2022. "Ecological and Environmental Effects of Land Use and Cover Changes on the Qinghai-Tibetan Plateau: A Bibliometric Review," Land, MDPI, vol. 11(12), pages 1-23, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlswr:v:18:y:2023:i:2:id:94-2022-swr. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.