Treated sewage quality prediction method based on combination of support vector classification and GRU neural network

Abstract

The invention discloses a treated sewage quality prediction method based on combination of support vector classification and a GRU neural network, and belongs to the technical field of sewage treatment. Missing value processing, abnormal value elimination and data standardization are carried out on the collected sewage historical data, a PCA principal component analysis method is adopted to carryout dimension reduction on the data, and the selected auxiliary variable is used as an input variable of a sewage quality prediction model; a sewage effluent key prediction model is established by adopting a GRU neural network suitable for processing time series data, a support vector machine model is firstly introduced to classify sewage quality data, and then the classified data is respectivelymodeled through the GRU neural network algorithm to predict effluent quality. When the SVM model is trained, a grid search method and a cross validation method are used for optimizing model parameters, the prediction precision of the obtained joint prediction model is more accurate, the model effect is better, the network performance can meet the actual application requirements, and accurate prediction of the effluent quality of the sewage treatment system can be realized.

Description

technical field [0001] The invention belongs to the technical field of sewage treatment, and in particular relates to a method for predicting the water quality of treated sewage based on the combination of support vector classification and GRU neural network. Background technique [0002] At present, the pollution problem in our country is becoming more and more serious, and more and more attention has been paid to sewage treatment. As an effective measure to protect water resources, the accurate detection of sewage effluent water quality has always been a problem because of its strong coupling and highly nonlinear characteristics in the sewage treatment process. For some For the measurement of key water quality parameters of effluent, the existing measurement methods or measuring instruments have many limitations and inconveniences, such as long detection cycle, expensive instruments, cumbersome operation and so on. [0003] Because of its advantages of good timeliness, hig...

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Problems solved by technology

[0002] At present, the pollution problem in our country is becoming more and more serious, and more and more attention has been paid to sewage treatment. As an effective measure to protect water resources, due to the strong coupling and highly nonlinear characteristics of the sewage treatment process, accurate detection of sewage effluent water quality has always been a problem. For some For the measurement of key water quality parameters of effluent, the existing measurement methods or measuring instruments have many limitations and inconveniences, such as long detection period, expensive instruments, cumbersome operation, etc.

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