Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Water quality data cleaning is important for the management of water environments. A framework for water quality time series cleaning is proposed in this paper. Considering the nonlinear relationships among water quality indicators, support vector regression (SVR) is used to forecast water quality indicators when some indicators are missing or when they show abnormal values at a certain point in time. Considering the time series of water quality information, long short-term memory (LSTM) networks are used to forecast water quality indicators when all indicators are missing at a certain point in time. A parallel model based on particle swarm optimization (PSO) and LSTM is realized based on a microservices architecture to improve the efficiency of model execution and the predictive accuracy of the LSTM networks. The performance of the model is evaluated in terms of the mean absolute error (MAE) and root-mean-square error (RMSE). Inlet water quality data from a wastewater treatment plant in Gaobeidian, Beijing, China is considered as a case study to examine the effectiveness of this approach. The experimental results reveal that this model has better predictive accuracy than other data-driven models because of smaller MAE and RMSE and has an advantage in terms of time consumption compared with standalone serial algorithms.