采用堆叠长短期记忆神经网络的水质连续预测方法

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关键词：余氯预测；水质参数预测；数据时序；长短期记忆神经网络中图分类号：TP31文献标志码：ADOI：10.7652/xjtuxb202506010 文章编号：0253-987X（2025）06-0093-10

Continuous Water Quality Prediction Method Based on Stacked Long Short-Term Memory Neural Networks

ZHANG Jianqi1'²，FENG Leyuan1 ，LI Donghel ，YANG Qingyu1，3 （1. Schoolof Automation Scienceand Engineering，Xi'an Jiaotong University，Xi'an71o049，China；2.Xi'an Aerospace Automation Co.，Ltd.，Xi'an 71oo65，China；3. State Key Laboratory For Manufacturing System Engineering， Xi'an Jiaotong University，Xi'an 7lo049，China）

Abstract： Aiming at the issues of abnormal water quality parameters and low prediction accuracy in water environment monitoring，this paper proposes a water quality parameter prediction model based on stacked long short-term memory neural network （SLSTM） to tackle the challenge of incomplete time series data. First，the timing characteristics of missing or abnormal water quality data were analyzed，and a deep neural network model for water quality prediction was designed based on stacked long short-term memory networks. Second， point-by-point prediction and multistep prediction methods were used to validate the proposed model in comparative experiments.

Lastly，in order to quantify the prediction performance of the model，two types of metrics were introduced， namely，the mean absolute percentage error （MAPE） and the root-mean-square error （RMSE） to assess the superiority of the SLSTM model over the support vector regression （SVR） and autoregressive integrated moving average （ARIMA） models. The experimental results showed that the prediction accuracy of SLSTM was significantly higher than that of the other two models in short-term （ 24h ）and long-term （ ⋅48h ） chlorine residual prediction： the MAPE of SLSTM was at least 9.15% lower than that of SVR for multistep prediction，and the RMSE of SLSTM was at least 31.25% lower than that of SVR for point-by-point prediction. In addition， compared with the ARIMA model， SLSTM can capture the nonlinear trend of water quality data more effectively and improve the prediction stability.This study not only verifies the effectiveness of SLSTM in water quality parameter prediction，but also provides new perspectives and tools for the field of water environment monitoring.

Keywords： chlorine residual prediction；water quality prediction;chronological data; long shortterm memory

随着城市人口规模的快速扩大，城市供水系统复杂性显著增加。（剩余14553字）