Real-time water quality detection based on fluctuation feature analysis with the LSTM model. Issue 1 (12th January 2023) - Wang, Lixiang; Dong, Huilin; Cao, Yuqi; Hou, Dibo; Zhang, Guangxin
Abstract: Signal analysis and anomaly detection for water pollution early warning systems are important and necessary. In view of the nonlinear and volatile characteristics of water quality time series, this paper proposes a new method for water anomaly detection based on fluctuation feature analysis. The method has two steps. First, the water quality time series data are used to calculate the residuals between the observed value and the predicted value with the long short-term memory (LSTM) network. Second, the dynamic features are extracted by sliding time window and described by the Approximate Entropy (ApEn) which are input to the anomaly detection model with Isolation Forest. Compared with traditional anomaly detection methods, the results obtained by the proposed method show better performance in distinguishing water quality anomalies. The proposed method can be applied to real-time water quality anomaly detection and early warning. HIGHLIGHTS: A prediction model based on LSTM networks is constructed to predict six water quality indicators. Dynamic features of water time series are extracted by the Approximate Entropy (ApEn). Combining with the high-dimensional ApEn characteristics, the Isolation Forest method is applied to identify anomalies of water quality. This research has the potential for the improvement of water quality early warning system. Graphical Abstract