基于MSLSTM-DA模型的水质自动监测异常数据报警

提出一种基于多元堆叠长短时记忆网络-差值分析(MSLSTM-DA)模型对地表水质异常数据进行报警的方法.该方法首先建立MSLSTM模型对水质指标数据进行预测,再基于预测结果的残差分布建立DA模型,并确定各个指标的数据异常阈值,当实测数据与预测数据差值大于阈值时进行数据报警.以长江流域监测断面的水质数据进行了方法有效性验证.结果表明,构建的预测模型对５个指标的MAE、MAPE均值比BP神经网络预测模型降低21.0%,17.8%,比LSTM模型降低16.8%,17.9%.皮尔逊系数均值比BP神经网络、LSTM模型的分别高5.9%,4.4%.5个指标共检出水质异常数据37条,其中34条经人工判断确实存在有异常,报警准确率高达91.9%.

Water quality alert with automatic monitoring data based on MSLSTM-DA model

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%.