Time-EAPCR: A Deep Learning-Based Novel Approach for Anomaly Detection Applied to the Environmental Field

2025-03-12

AI Summary

Traditional environmental monitoring methods suffer from delayed response, poor generalizability, and difficulty modeling long-term temporal dependencies and high variability. To address these limitations, this paper proposes Time-EAPCR—the first end-to-end anomaly detection framework integrating time embedding, self-attention, permutation convolution, and residual learning—explicitly capturing spatiotemporal evolution patterns and multidimensional feature correlations in environmental data. The architecture significantly enhances robustness and interpretability of anomaly discrimination. Evaluated on four public environmental datasets, it achieves an average 12.6% improvement in F1-score. Deployment feasibility is further validated in a real-world river monitoring system. Experimental results demonstrate strong cross-scenario generalization capability, establishing a novel paradigm for intelligent anomaly monitoring in aquatic ecosystems and water treatment infrastructure.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• Detects anomalies in environmental systems using deep learning.
• Addresses limitations of traditional environmental monitoring methods.
• Improves accuracy and adaptability in multi-source environmental data analysis.

Innovation

• Time-EAPCR: deep learning for anomaly detection
• Captures temporal patterns in environmental data
• Validated on multiple datasets for robustness