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Machine learning models for fine characterization of landfill leakage and uncertainty analysis
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Chang-xin NAI1, 2, Hui-min ZHANG1, 2, Xiao-chen SUN1, *, Ya XU2, 3, *, Jing-cai LIU2, Yu-qiang LIU2
China Environmental Science | 2025, 45(9) : 4986 - 4996
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China Environmental Science | 2025, 45(9): 4986-4996
• Solid Waste •
Machine learning models for fine characterization of landfill leakage and uncertainty analysis
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Chang-xin NAI1, 2, Hui-min ZHANG1, 2, Xiao-chen SUN1, *, Ya XU2, 3, *, Jing-cai LIU2, Yu-qiang LIU2
Affiliations
  • 1.School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China
  • 2.Chinese Research Academy of Environmental Sciences, Beijing 100012, China
  • 3.Key Laboratory of Industrial Waste Recycling and Regulation, Gansu Province, Lanzhou 730000, China
Published: 2025-09-20
Outline
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This study addresses the limitations of traditional landfill groundwater pollution risk prediction methods by integrating high-performance simulation with uncertainty analysis to more accurately capture the effects of concealed leakage. The limitations of high computational load and low efficiency were addressed by combining a Particle Swarm Optimization (PSO) algorithm with an Extreme Gradient Boosting (XGBoost) model. By learning from multiple samples generated by detailed simulation models, a surrogate model was established to improve prediction efficiency. Additionally, an improved sampling method was proposed to reduce the reliance of PSO-XGBoost on sample size. The results indicate that the machine learning surrogate model developed in this study better captures the impact of parameter uncertainty compared to traditional analytical-Monte Carlo methods such as Landsim. The predicted pollution concentration range is 1.36 times greater than that of conventional models. The PSO-XGBoost surrogate model, combined with the improved sampling method, outperforms other machine learning models in prediction accuracy (R2= 0.99) and stability, while reducing the computational time for model training by 70%, significantly improving the efficiency of uncertainty analysis. The application research based on this model shows that the probability of the characteristic pollutant COD exceeding the standard in the 10th year reaches 56.43%, and the possible distance of exceeding the standard is 54meters, indicating that there is a certain pollution risk in the study area. These findings provide an efficient and precise scientific method for analyzing landfill leakage risks and uncertainties and offer valuable references for further related research.

uncertainty analysis  /  surrogate model  /  numerical simulation  /  landfill  /  risk assessment
能昌信, 张慧敏, 孙晓晨, 徐亚, 刘景财, 刘玉强. 机器学习模型精细表征填埋场渗漏风险及其不确定性. 中国环境科学, 2025 , 45 (9) : 4986 -4996 .
Chang-xin NAI, Hui-min ZHANG, Xiao-chen SUN, Ya XU, Jing-cai LIU, Yu-qiang LIU. Machine learning models for fine characterization of landfill leakage and uncertainty analysis[J]. China Environmental Science, 2025 , 45 (9) : 4986 -4996 .
Year 2025 volume 45 Issue 9
71
19
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Article Info
  • Receive Date:2025-02-21
  • Online Date:2026-10-08
  • Published:2025-09-20
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  • Received:2025-02-21
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Affiliations
    1.School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China
    2.Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    3.Key Laboratory of Industrial Waste Recycling and Regulation, Gansu Province, Lanzhou 730000, China
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表12种不同金属材料的力学参数
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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