Influent water quality conditions are the key elements required to investigate and optimize the management of sewage treatment plants

and timely acquisition of influent water quality data is of vital importance. In view of the fact that five-day biochemical oxygen demand (BOD5)

a key water quality indicator of sewage plants

is difficult to directly detect and has strong hysteresis

four methods including the back-propagation artificial neural network (BP-ANN)

grid search algorithm (GS) optimized support vector regression (SVR)

particle swarm optimization (PSO) improved SVR

and genetic algorithm (GA) improved SVR were adopted to establish soft sensing models of influent BOD5 by using the mathematical relationship between BOD5 and other influent parameters to achieve the rapid determination of influent BOD5. A sewage plant in Heilongjiang province was taken as the research object

and the performance of the four machine learning models was compared to find a soft sensing method suitable for the prediction of influent BOD5. Results show that the prediction results of the soft sensing model based on SVR were better than that based on BP-ANN

and the GA optimized SVR model had the highest accuracy

which provides reference for the real-time monitoring of BOD5 and convenient management of sewage treatment plants.