Machine learning brings new insights for reducing salinization disaster
نویسندگان
چکیده
This study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that humidity and subterranean CO 2 concentration are two leading controls salinity—respectively explain 71.33%, 13.83% data. The ( R , root-mean-square error, RPD) values at training stage, validation stage testing (0.9924, 0.0123, 8.282), (0.9931, 0.0872, 7.0918), (0.9826, 0.1079, 6.0418), respectively. Based on underlining mechanisms, we conjecture sequestration could reduce salinization disaster
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ژورنال
عنوان ژورنال: Frontiers in Earth Science
سال: 2023
ISSN: ['2296-6463']
DOI: https://doi.org/10.3389/feart.2023.1130070