Predicting maturity and identifying key factors in organic waste composting using machine learning models.
Bioresour Technol
; 400: 130663, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38583671
ABSTRACT
The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Compostagem
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Redes Neurais de Computação
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Aprendizado de Máquina
Idioma:
En
Revista:
Bioresour Technol
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China