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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.

, authors=Zhao-long WANG^{1, 2, 3}, Yu-hui PENG⁴, Pei-fan YAO⁴, Zhi SUN³, Xi-hua ZHANG^{4, *}, Yang ZHENG^{1, **}, authorsList=Zhao-long WANG, Yu-hui PENG, Pei-fan YAO, Zhi SUN, Xi-hua ZHANG, Yang ZHENG, authorCompany=null, correspAuthors=Xi-hua ZHANG, Yang ZHENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314918287893430681, articleId=1314918287318811031, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=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, columnId=1240689598737731645, journalTitle=中国环境科学, columnName=固体废物, runingTitle=null, highlight=null, articleAbstract=

为提高出口风险预警的准确性和科学性，构建了一种基于K-means聚类与粒子群优化BP（PSO-BP）神经网络的危险废物出口风险预警系统．基于历史数据的多维指标，采用K-means聚类算法对危险废物出口风险进行分类，并运用PSO算法优化BP神经网络的初始参数，提高风险预测的精度和收敛效率．实验结果表明，K-means聚类的风险分类与专家标注的一致性达93.2%，证明该方法能够有效揭示数据的内在风险结构；PSO-BP神经网络的预测准确率达92.5%，均方误差降低至0.012，相较于传统BP神经网络，预测精度显著提升，训练收敛速度提高40%．基于上述模型，本研究开发了一套危险废物出口风险预警系统，实现数据采集、风险评估、模型预测与预警可视化等功能，并以30类危险废物为例验证了模型的实现与应用．结果表明，系统能够实时分析危险废物出口数据，依据风险预测结果提供动态预警，从而有效提升危险废物出口管理工作的科学性和效率．

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王兆龙（1987-），男，河南洛阳人，中国科学院大学博士研究生，研究方向为固废资源化及污染防治．发表论文20余篇.

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王兆龙（1987-），男，河南洛阳人，中国科学院大学博士研究生，研究方向为固废资源化及污染防治．发表论文20余篇.

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