Water Quality Anomaly Detection with Dual Sliding Windows and Convolutional LSTM

Water pollution is continuously increasing in water ecosystems across all continents. Surface water sensors can record data on water quality indicators at regular intervals, and the associated water quality sequences show abnormal trends when extreme weather or unusual industrial discharges occur. Therefore, governments can take timely actions to minimize damage and protect the water environment by detecting these abnormal trends promptly. However, current methods make it difficult to interpret different correlations among water quality parameters effectively. To solve this problem, this work proposes a parameter correlation-aware anomaly detection model, which integrates Dual sliding windows, Convolutional LSTM, and a Deep neural network with dropout, called for DCLD short. First, DCLD designs dual sliding windows to capture local and global patterns within the sequence of water quality. Second, DCLD adopts a stacked long short-term memory with a convolutional neural network to capture complex features and long-term dependencies in the time series. Third, DCLD uses a deep neural network incorporating the dropout algorithm to extract abstract features. DCLD mitigates overfitting risks and enhances the model's generalization capacity. Finally, DCLD is evaluated with two real-world water quality datasets, and its anomaly detection accuracy is improved by 5.41% and 0.79% on average over its peers.