Prediction of available phosphorus formation and analysis of key influencing factors during organic waste composting using stacking ensemble learning

DAI Xu; YANG Xiaofan; QU Jing; ZHANG Meng; GAO Xiaozhong; CHENG Xiang; YANG Tianxue; SUN Dezhi

doi:10.13205/j.hjgc.202608006

Volume 44 Issue 8

Aug. 2026

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DAI Xu, YANG Xiaofan, QU Jing, ZHANG Meng, GAO Xiaozhong, CHENG Xiang, YANG Tianxue, SUN Dezhi. Prediction of available phosphorus formation and analysis of key influencing factors during organic waste composting using stacking ensemble learning[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(8): 51-62. doi: 10.13205/j.hjgc.202608006

Citation:

DAI Xu, YANG Xiaofan, QU Jing, ZHANG Meng, GAO Xiaozhong, CHENG Xiang, YANG Tianxue, SUN Dezhi. Prediction of available phosphorus formation and analysis of key influencing factors during organic waste composting using stacking ensemble learning[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(8): 51-62. doi: 10.13205/j.hjgc.202608006

Citation:

DAI Xu, YANG Xiaofan, QU Jing, ZHANG Meng, GAO Xiaozhong, CHENG Xiang, YANG Tianxue, SUN Dezhi. Prediction of available phosphorus formation and analysis of key influencing factors during organic waste composting using stacking ensemble learning[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(8): 51-62. doi: 10.13205/j.hjgc.202608006

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Prediction of available phosphorus formation and analysis of key influencing factors during organic waste composting using stacking ensemble learning

doi: 10.13205/j.hjgc.202608006

1. College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China;
2. The Institute of Solid Waste Pollution Control Technology, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Received Date: 2026-03-19

Abstract

Abstract

Organic waste contains abundant phosphorus （P） and is regarded as a potential P reservoir. The variation in available phosphorus （AP） during its resource utilization is of great importance for phosphorus recovery and utilization. Composting is an important approach for the resource utilization of organic waste， during which P speciation is transformed along with the degradation and humification of organic matter. However， because composting experiments are time-consuming and costly， and the process is influenced by multiple factors， the dynamic behavior of AP is difficult to reveal efficiently using conventional experiments. In this study， a dataset containing 647 samples was compiled from the literature. Data preprocessing was implemented via iterative imputation， one-hot encoding， and standardization. A predictive model for AP formation during composting was developed using a stacking ensemble learning method. Shapley additive explanations （SHAP） and partial dependence plots （PDPs） were further used to identify the key factors affecting AP formation. The results showed that the best ensemble model for predicting AP during organic waste composting consisted of XGBoost and SVR as base learners and ElasticNet as the meta-learner. The model achieved R² values of 0.954 and 0.928 for the training and test sets， respectively， indicating stable predictive performance and low overall error. Global SHAP importance analysis showed that the main factors affecting AP content were feedstock type， bulking agent type， turning interval， pH， electrical conductivity （EC）， and the C/N ratio. In particular， livestock manure as the feedstock and straw-based materials as the bulking agent made significant positive contributions to the predicted AP values. PDP analysis further showed that relatively low pH and C/N ratio generally favored AP accumulation throughout the composting period. During the initial stage of composting， higher moisture content and lower EC were more favorable for AP increase. During the early thermophilic stage， higher pile temperature was associated with higher predicted AP levels. During the cooling and maturation stage， AP accumulation was further promoted when moisture content （< 48%） and C/N ratio （< 14） were kept at relatively low levels and the composting duration was appropriately extended （> 43 d）. In this study， accurate prediction of AP content during organic waste composting was achieved using a stacking ensemble learning method， and the key factors affecting AP formation were identified through SHAP and PDP analyses. The developed model provides support for regulating AP formation and optimizing engineering applications in the composting process.
- ensemble learning,
- composting,
- available phosphorus （AP）,
- model interpretability,
- SHAP analysis

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References(47)

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