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Research and application of hazardous waste export risk warning model based on K-means clustering and PSO-BP neural network
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Zhao-long WANG1, 2, 3, Yu-hui PENG4, Pei-fan YAO4, Zhi SUN3, Xi-hua ZHANG4, *, Yang ZHENG1, **
China Environmental Science | 2025, 45(10) : 5619 - 5626
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China Environmental Science | 2025, 45(10): 5619-5626
• Solid Waste •
Research and application of hazardous waste export risk warning model based on K-means clustering and PSO-BP neural network
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Zhao-long WANG1, 2, 3, Yu-hui PENG4, Pei-fan YAO4, Zhi SUN3, Xi-hua ZHANG4, *, Yang ZHENG1, **
Affiliations
  • 1.Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China
  • 2.University of Chinese Academy of Sciences, Beijing 100049, China
  • 3.Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
  • 4.School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
Published: 2025-10-20
Outline
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Improper export of hazardous waste may cause serious environmental pollution and regulatory risks. To improve the accuracy and scientific nature of export risk warning, this study constructed a hazardous waste export risk warning system based on K-means clustering and particle swarm optimization BP (PSO-BP) neural network. Based on multidimensional indicators of historical data, the export risk of hazardous waste was classified using the K-means clustering algorithm, and the initial parameters of the BP neural network were optimized by the PSO algorithm, thereby improving the accuracy and convergence efficiency of risk prediction. The experimental results showed that the consistency between the risk classification by K-means clustering and expert annotation was 93.2%, proving that the inherent risk structure of the data could be effectively revealed by this method. The prediction accuracy of the PSO-BP neural network reached 92.5%, and the mean square error was reduced to 0.012. Compared with the traditional BP neural network, the prediction accuracy was significantly improved, and the training convergence speed was increased by 40%. Based on the above model, a hazardous waste export risk warning system was developed in this study, through which data collection, risk assessment, model prediction, and warning visualization were achieved. The implementation and application of the model were verified using 30 types of hazardous waste as examples. The results showed that hazardous waste export data could be analyzed in real time, dynamic warnings could be provided based on risk prediction results, and the efficiency of hazardous waste export management could be effectively improved.

hazardous waste  /  export risk  /  K-means clustering  /  BP neural network  /  particle swarm optimization  /  risk warning
王兆龙, 彭雨卉, 姚沛帆, 孙峙, 张西华, 郑洋. 基于K-means聚类与PSO-BP神经网络的危险废物出口风险预警模型研究及应用. 中国环境科学, 2025 , 45 (10) : 5619 -5626 .
Zhao-long WANG, Yu-hui PENG, Pei-fan YAO, Zhi SUN, Xi-hua ZHANG, Yang ZHENG. Research and application of hazardous waste export risk warning model based on K-means clustering and PSO-BP neural network[J]. China Environmental Science, 2025 , 45 (10) : 5619 -5626 .
Year 2025 volume 45 Issue 10
72
21
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Article Info
  • Receive Date:2025-03-24
  • Online Date:2026-10-08
  • Published:2025-10-20
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  • Received:2025-03-24
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Affiliations
    1.Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment, Beijing 100029, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
    4.School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
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表12种不同金属材料的力学参数
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Family
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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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