Using a Novel Algorithm Based on the Random Vector Functional Link Network and Multi-Verse Optimizer to Forecast Effluent Quality

Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application

The accurate prediction and assessment of effluent quality in wastewater treatment plants (WWTPs) are paramount for the efficacy of sewage treatment processes. Neural network models have exhibited promise in enhancing prediction accuracy by simulating and analyzing diverse influent parameters. In this study, a back propagation neural network hybrid model based on a tent chaotic map and sparrow search algorithm (Tent_BP_SSA) was developed to predict the effluent quality of sewage treatment processes. The prediction performance of the propose hybrid model was compared with traditional neural network models using five performance indicators (MAE, RMSE, SSE, MAPE and R2). Specifically, in comparison with the prior Tent_BP_SSA, Tent_BP_SSA2 demonstrated notable enhancements, with the R2 increasing from 0.9512 to 0.9672, while MAE, RMSE, SSE, and MAPE decreased by 9.62%, 18.84%, 24.80%, and 47.10%, respectively. These indicators collectively affirm that the utilization of higher-order input parameters ensures improved accuracy of the Tent_BP_SSA2 hybrid model in predicting effluent quality. Moreover, the Tent_BP_SSA2 model exhibited robust prediction ability (R2 of 0.9246) when applied to assess the effluent quality of an actual sewage treatment plant. The incorporation of integrated models comprising the sparrow search optimizing algorithm, tent chaotic mapping, and higher-order magnitude decomposition of input parameters has demonstrated the capacity to enhance the accuracy of effluent quality prediction. This study illuminates novel perspectives on the prediction of effluent quality and the assessment of effluent warnings in WWTPs.

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

The economic development of various regions is influenced by high-quality population mobility. The research object of this article is the employment mobility data of “Double First-Class” university graduates from 2014 to 2019; the subsequent analysis is based on these data. First, this paper summarizes the current state of university graduates’ employment mobility. Second, this paper employs the fixed-effect model and PCA method to conclude that economic factors are the primary factors influencing university graduates’ employment mobility. Finally, based on the nonlinear, small sample, and high-dimensional characteristics of university graduates’ employment mobility data, this paper employs the random forest and BP neural network methods to build a prediction model for university graduates’ employment retention rate. The results show that the BP neural network model outperforms the random forest model in terms of prediction accuracy. The BP neural network model can accurately predict the employment retention rate of “Double First-Class” university graduates, which can guide the reasonable mobility of university graduates and provide a reference for government universities and individuals to make decisions.