Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning

Soil extracellular electron transfer (EET) is a pivotal biological process within the realm of soil. Unfortunately, EET suffers from a lack of predictive models. Herein, an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemic...

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Veröffentlicht in:	Science China. Technological sciences 2024, Vol.67 (1), p.259-270
Hauptverfasser:	Ou, JiaJun, Luo, XiaoShan, Liu, JunYang, Huang, LinYan, Zhou, LiHua, Yuan, Yong
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Biological activity Dependent variables Electron transfer Engineering Independent variables Machine learning Microorganisms Multilayer perceptrons Organic carbon Physiochemistry Prediction models Soil properties Soils Support vector machines
Online-Zugang:	Volltext
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