Abstract：A multivariate stacked long and short term memory network-difference analysis (MSLSTM-DA) model is proposed to alarm surface water quality abnormal data. Established the MSLSTM model to predict the water quality data, and then established the DA model based on the residual distribution of the prediction results to determine the threshold value of each indicator, and alerted the data when the difference between the measured data and the predicted data is greater than the threshold value. The validity of the method was verified using water quality data from the Yangtze River basin monitoring sections. The results showed that the mean values of MAE and MAPE for five indicators were 21.0% and 17.8% lower than those of BP neural network prediction model, and 16.8% and 17.9% lower than those of LSTM model. The mean value of Pearson coefficient was 5.9% and 4.4% higher than that of BP neural network and LSTM model. 37 abnormal water quality data were detected for the 5 indicators, 34 of which were judged to be abnormal by manual judgment, with an alarm accuracy rate of 91.9%.
嵇晓燕, 姚志鹏, 杨凯, 陈亚男, 王正, 安新国. 基于MSLSTM-DA模型的水质自动监测异常数据报警[J]. 中国环境科学, 2022, 42(4): 1877-1883.
JI Xiao-yan, YAO Zhi-peng, YANG Kai, CHEN Ya-nan, WANG Zheng, AN Xin-guo. Water quality alert with automatic monitoring data based on MSLSTM-DA model. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(4): 1877-1883.
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