Machine learning models for fine characterization of landfill leakage and uncertainty analysis

NAI Chang-xin; ZHANG Hui-min; SUN Xiao-chen; XU Ya; LIU Jing-cai; LIU Yu-qiang

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China Environmental Science ›› 2025, Vol. 45 ›› Issue (9) : 4986-4996.

Solid Waste

Machine learning models for fine characterization of landfill leakage and uncertainty analysis

NAI Chang-xin^1,2, ZHANG Hui-min^1,2, SUN Xiao-chen¹, XU Ya^2,3, LIU Jing-cai², LIU Yu-qiang²

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Abstract

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.

Key words

uncertainty analysis / surrogate model / numerical simulation / landfill / risk assessment

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NAI Chang-xin, ZHANG Hui-min, SUN Xiao-chen, XU Ya, LIU Jing-cai, LIU Yu-qiang. Machine learning models for fine characterization of landfill leakage and uncertainty analysis[J]. China Environmental Science. 2025, 45(9): 4986-4996

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