Construction of pollution risk early warning model for urban drinking water supply chain

Abstract

Abstract In order to improve the efficiency of urban drinking water safety monitoring and early warning management, a pollution risk early warning model of urban drinking water supply chain is proposed. Firstly, the current situation of urban drinking water supply is analyzed and the causes of pollution are analyzed. Then, the autoregressive model is used to predict the time series of multiple water quality indicators by constantly introducing new monitoring data modes for the residual vector group, the outlier scores of each vector group are obtained by using the isolated forest algorithm to judge whether the water quality is abnormal or not, and the fuzzy comprehensive evaluation method is used to evaluate the level of the abnormal situation and carry out the corresponding level early warning. The experimental results show that the area under the receiver operating characteristic curve can reach 0.919 when using the prediction residual vector group of turbidity and conductivity to detect the numerical changes of water quality parameters in the drinking water supply chain, accurately predict the abnormal data, make early warning, and provide the guarantee for the survival of urban residents and urban development.