| Water quality anomaly detection is a crucial component of water environment system protection.However,the current detection methods suffer from issues such as incomplete data analysis,reliance on a single index,and insufficient emergency response capability.Therefore,it is of great significance to build an effective early warning and monitoring model to detect the change of water environment in time,and to analyze the fluctuation characteristics of water quality data using data mining,pattern analysis,and other methods.This paper analyzes the water quality time series data by combining the deep learning network model and clustering algorithm.The main research work includes the following three parts:(1)Build water quality prediction model.In order to address the issues of the traditional model,such as insufficient correlation analysis between water quality indicators,insufficient mining of time series rules,and slow convergence speed.A hybrid water quality prediction model based on CNN-EA-Conv LSTM is proposed,and experimental simulation analysis is carried out.The results show that the method can better overcome the influence of water quality background data fluctuations and identify the nonlinear changes of the signal.Additionally,the experiment demonstrates that the developed water quality prediction model improves the expression ability of time series data by enhancing the attention given to important time nodes and learning spatiotemporal data characteristics,resulting in high accuracy.(2)Optimizing water quality prediction models.Considering the varying impact of hyperparameters on the performance of neural network models,this paper proposes an evolutionary algorithm to find the optimal combination of parameters,and uses the global search ability of the genetic algorithm to find the optimal values of hyperparameters such as the number of hidden layers,training epochs,window size,learning rate and other superparameters.Furthermore,the optimized water quality prediction model is used for anomaly detection.Simulation results indicate that the optimized parameters maximize the model’s performance and effectively enhance its prediction ability.(3)Water quality data analysis and processing.By analyzing the characteristics of time series data,this study proposes a water quality feature extraction method that combines Empirical Wavelet Transform(EWT)and Kernel Principal Component Analysis(KPCA)to addre ss the complex fluctuation characteristics and high false alarm rate of water quality data anomaly detection.Fuzzy clustering(KFCM)is utilized to output the water quality anomaly detection results.The simulation results demonstrate that the proposed method can effectively capture the characteristics of water quality fluctuations at different scales,accurately detect water environment anomalies,and reduce the false alarm rate. |
