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Predicting maturity and identifying key factors in organic waste composting using machine learning models.

Ning Wang1, Wanli Yang1, Bingshu Wang2

  • 1Shenzhen Engineering Laboratory for Eco-efficient Recycled Materials, School of Environment and Energy, Peking University, Shenzhen Graduate School, University Town, Xili, Nanshan District, Shenzhen 518055, China.

Bioresource Technology
|April 7, 2024
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Summary

Predicting the germination index (GI) in composting is now faster with machine learning. Random Forest and Artificial Neural Network models accurately forecast GI using composting parameters like time and temperature.

Keywords:
Artificial Neural NetworkComposting timeGermination indexRandom ForestSHapley additive exPlanations

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Area of Science:

  • Environmental Science
  • Agricultural Science
  • Data Science

Background:

  • Measuring the germination index (GI) in composting is currently a slow and resource-intensive process.
  • Accurate GI measurement is crucial for assessing compost maturity and quality.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting the germination index (GI) in composting.
  • To identify key composting parameters influencing GI prediction.

Main Methods:

  • Four ML models were employed: Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT).
  • Models were trained and validated using composting parameters to predict GI.
  • SHapley additive exPlanations (SHAP) were used to determine feature importance.

Main Results:

  • RF and ANN models demonstrated high predictive performance, with R² values exceeding 0.9.
  • SVR (<0.6) and DT (<0.8) showed lower predictive accuracy for GI.
  • Composting time, temperature, and pH were identified as significant factors influencing GI, with composting time being the most impactful.

Conclusions:

  • RF and ANN are effective ML tools for accurately predicting GI in composting processes.
  • This approach enables more efficient and intelligent composting management.
  • The study provides a reliable method for rapid GI assessment, optimizing compost quality control.