Anomaly Detection of IoT Water Quality Monitoring Data Based on Explainable Deep Learning
With the increasing applicability of Internet-of-Things(IoT)technology,the number and types of IoT devices and sensors are continuously increasing.In particular,IoT water quality sensors play a vital role in the field of ecological monitoring and protection.Accordingly,this study proposes an unsupervised anomaly data detection algorithm based on explainable deep learning to address the issues of large volume,high dimensionality,and lack of labeling in the monitoring data collected by IoT water quality sensors.The algorithm uses the Auto-Encoder(AE)and SHAP algorithms to detect anomalies in multi-dimensional water quality datasets.The AE model is trained to flag data with significant reconstruction errors,and SHAP is used to interpret the AE and calculate the importance of each feature in the flagged data.Based on the importance of these features,the final anomaly value is determined for the dataset.Experimental results on an IoT water quality monitoring dataset show that the algorithm can effectively detect anomalous data with an Fl value of 0.875,outperforming existing unsupervised anomaly detection algorithms.Thus,the proposed algorithm has a practical application value for processing IoT water quality monitoring data.Furthermore,the algorithm can be applied to the anomaly detection of massive IoT monitoring data in other fields,such as meteorology and the environment.
deep learningAuto-Encoder(AE)anomaly detectionexplainable machine learningunsupervised learning
万方数据
维普
