Machine-learning-assisted process interpretation of composting time across composting systems involving multiple organic wastes: Key explanatory contributions of temperature evolution during the cooling–maturation phase

Xia Yao

doi:10.1016/j.jenvman.2026.130697

Machine-learning-assisted process interpretation of composting time across composting systems involving multiple organic wastes: Key explanatory contributions of temperature evolution during the cooling–maturation phase

2026-08-12

0

PRE

AI

X

Xia Yao

W

Weilin Fu

宋春 (Chun Song)

X

Xueyan Zhang

S

Shiyu Lv

Y

Yongzhen Ding *

王

王凤 (Feng Wang) *

DOI：10.1016/j.jenvman.2026.130697

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• After grouped validation and hyperparameter optimization, XGBoost achieved a mean test R2 of 0.77 for composting time. • Cooling–maturation-phase cumulative temperature had the greatest contribution to the model output for composting time. • SEM showed the strongest pathfor cooling–maturation-phase cumulative temperature with composting time (β = 0.80, p < 0.001).

Machine-learning-assisted process interpretation of composting time across composting systems involving multiple organic wastes: Key explanatory contributions of temperature evolution during the cooling–maturation phase

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