AUTHOR=An Peng , Wang Wenfeng , Chen Xi , Zhuang Zhikai , Cui Lujie 

TITLE=Machine learning brings new insights for reducing salinization disaster

JOURNAL=Frontiers in Earth Science

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1130070

DOI=10.3389/feart.2023.1130070

ISSN=2296-6463

ABSTRACT=<p>This study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that soil humidity and subterranean CO<sub>2</sub> concentration are two leading controls of soil salinity—respectively explain 71.33%, 13.83% in the data. The (<italic>R</italic><sup>2</sup>, root-mean-square error, RPD) values at the training stage, validation stage and testing stage are (0.9924, 0.0123, and 8.282), (0.9931, 0.0872, and 7.0918), (0.9826, 0.1079, and 6.0418), respectively. Based on the underlining mechanisms, we conjecture that subterranean CO<sub>2</sub> sequestration could reduce salinization disaster in deserts.</p>