自组织映射助力湖泊水库水质评估，挖掘数据隐含规律
Self-organizing maps for water quality assessment in reservoirs and lakes: A systematic literature review
- 用自组织映射分析多维水质数据，发现隐藏模式
- 在无标签数据下仍能有效识别关键水质关联
- 适合生态评估与藻华监测，支持可持续管理
可持续的水质是生态平衡与水安全的基础。湖泊和水库的水质评估与管理因数据稀疏、参数异质性及非线性关系而困难重重。本文系统综述了自组织映射（SOM）这一无监督人工智能技术在水质评估中的应用，整合了参数选择、时空采样策略及聚类方法的研究。重点探讨了SOM如何处理高维数据，揭示隐藏生态规律，支持高效管理决策。随着现场传感器、遥感影像、物联网技术和历史记录的日益丰富，环境监测的分析机会显著拓展。SOM已在复杂数据集分析中表现优异，尤其在缺乏标注数据时仍具优势，可实现高维数据可视化，辅助发现水质指标间的深层关联。本综述凸显了SOM在生态评估、营养状态分类、藻华监测及流域影响评估中的广泛应用潜力。研究结果为未来相关领域的方法优化与实际应用提供了全面参考。
原文摘要 · Abstract (English)
Sustainable water quality underpins ecological balance and water security. Assessing and managing lakes and reservoirs is difficult due to data sparsity, heterogeneity, and nonlinear relationships among parameters. This review examines how Self-Organizing Map (SOM), an unsupervised AI technique, is applied to water quality assessment. It synthesizes research on parameter selection, spatial and temporal sampling strategies, and clustering approaches. Emphasis is placed on how SOM handles multidimensional data and uncovers hidden patterns to support effective water management. The growing availability of environmental data from in-situ sensors, remote sensing imagery, IoT technologies, and historical records has significantly expanded analytical opportunities in environmental monitoring. SOM has proven effective in analysing complex datasets, particularly when labelled data are limited or unavailable. It enables high-dimensional data visualization, facilitates the detection of hidden ecological patterns, and identifies critical correlations among diverse water quality indicators. This review highlights SOMs versatility in ecological assessments, trophic state classification, algal bloom monitoring, and catchment area impact evaluations. The findings offer comprehensive insights into existing methodologies, supporting future research and practical applications aimed at improving the monitoring and sustainable management of lake and reservoir ecosystems.
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