Dynamic decision-making for inspecting the quality of treated sewage

Given the scarcity of fresh water, aquatic ecosystems influence climate change. Municipal wastewater pumped into the sewage treatment system could then begin to be re-utilized, balancing how fresh water can be reused with how to fully utilize the potential value of water resources. The crux of the drainage standard is that the decision-making model is accurate and effective for detecting the quality of treated sewage and improving sewage treatment measures. The proposed method called PRBF-SVM(Parallel Radial Basis Function Support Vector Machine) predicts the classification of disposed sewage and provides an effective and efficient solution for detecting water quality. To solve the decision-making problem in municipal wastewater systems, a relevant decision model is set up to combine multi-attributes, and parallel grid search and cross-validation are employed to obtain the optimal model on the training data. Experimental results show that the proposed method improves the accuracy of classification prediction, which is higher than that of the original algorithm. In addition, the performance of the run-time and ac-curacy are perfect, compared to logistic regression, decision tree, random forest, KNeighbours, XGboost, and AdaBoost methods on the same dataset. PRBF-SVM can be used to determine whether sewage is disposed of to meet the post-treatment drainage standard.