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

Xiangwei Wang, Yizhe Yang, Jianglong Lv and Hailong He
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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
Hailong He: College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, P.R. China

Soil and Water Research, 2023, vol. 18, issue 2, 67-80

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.

Keywords: machine learning; science mapping; scientometric analysis; soil (search for similar items in EconPapers)
Date: 2023
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DOI: 10.17221/94/2022-SWR

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