采用堆疊長短期記憶神經(jīng)網(wǎng)絡(luò)的水質(zhì)連續(xù)預(yù)測方法

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關(guān)鍵詞：余氯預(yù)測；水質(zhì)參數(shù)預(yù)測；數(shù)據(jù)時序；長短期記憶神經(jīng)網(wǎng)絡(luò)中圖分類號：TP31文獻標(biāo)志碼：ADOI：10.7652/xjtuxb202506010 文章編號：0253-987X（2025）06-0093-10

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

ZHANG Jianqi1'2，F(xiàn)ENG 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

隨著城市人口規(guī)模的快速擴大，城市供水系統(tǒng)復(fù)雜性顯著增加。（剩余14553字）