用深度学习提升环境异常检测精度，解决数据复杂性难题。
Time-EAPCR: A Deep Learning-Based Novel Approach for Anomaly Detection Applied to the Environmental Field
- 提出Time-EAPCR模型，融合时间嵌入与注意力机制捕捉时序特征。
- 在四个公开数据集上准确率显著提升，跨场景适应性强。
- 适合环境监测、生态保护等需要实时预警的领域使用。
随着人类活动加剧，水生生态系统和污水处理系统面临日益复杂的压力，影响生态平衡、公共健康与可持续发展，智能异常监测变得至关重要。传统监测方法存在响应延迟、数据处理能力不足及泛化能力弱等问题，难以满足复杂环境监测需求。近年来，机器学习虽被广泛用于异常检测，但环境生态数据的多维特征、时空动态性，尤其是时间维度上的长期依赖与强变异性，限制了传统方法的效果。深度学习具备自动提取特征的能力，可捕捉复杂非线性关系，提升检测性能，但在环境监测中的应用仍处初期，需进一步探索。本文提出一种新型深度学习方法Time-EAPCR（Time-Embedding-Attention-Permutated CNN-Residual），用于揭示特征相关性，捕捉时间演化模式，实现对环境系统的精准异常检测。在四个公开环境数据集上验证了其高精度与鲁棒性。实验结果表明，该方法能高效处理多源数据，提升检测准确率，在多种场景下表现优异，具备强适应性与泛化能力。此外，基于真实河流监测数据集的测试证实了其部署可行性，为环境监测提供了可靠技术支撑。
原文摘要 · Abstract (English)
As human activities intensify, environmental systems such as aquatic ecosystems and water treatment systems face increasingly complex pressures, impacting ecological balance, public health, and sustainable development, making intelligent anomaly monitoring essential. However, traditional monitoring methods suffer from delayed responses, insufficient data processing capabilities, and weak generalisation, making them unsuitable for complex environmental monitoring needs.In recent years, machine learning has been widely applied to anomaly detection, but the multi-dimensional features and spatiotemporal dynamics of environmental ecological data, especially the long-term dependencies and strong variability in the time dimension, limit the effectiveness of traditional methods.Deep learning, with its ability to automatically learn features, captures complex nonlinear relationships, improving detection performance. However, its application in environmental monitoring is still in its early stages and requires further exploration.This paper introduces a new deep learning method, Time-EAPCR (Time-Embedding-Attention-Permutated CNN-Residual), and applies it to environmental science. The method uncovers feature correlations, captures temporal evolution patterns, and enables precise anomaly detection in environmental systems.We validated Time-EAPCR's high accuracy and robustness across four publicly available environmental datasets. Experimental results show that the method efficiently handles multi-source data, improves detection accuracy, and excels across various scenarios with strong adaptability and generalisation. Additionally, a real-world river monitoring dataset confirmed the feasibility of its deployment, providing reliable technical support for environmental monitoring.
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