Genetic algorithm optimized back propagation artificial neural network for a study on a wastewater treatment facility cost model

In this study, the genetic algorithm optimized back propagation artificial neural network (GA-BP -ANN) method is used to predict the cost of a wastewater treatment plant. With biological oxy-gen demand, design volume, catchment area and treatment degree as input data, the total cost and construction cost as output parameters, the cost of a wastewater treatment plant is simu-lated. Compared with the linear algorithm commonly used before, this method has the follow-ing advantages: (1) GA-BP-ANN is suitable for small sample analysis and can effectively improve the stability of data. (2) Remove the influence of subjectivity and provide better help for decision makers. The effectiveness and feasibility of this method are proved theoretically and verified by simulation. The results can provide guidance for the design and operation of sewage treatment plants.

Automated summary

In this study, the GA-BP-ANN method is used to predict the cost of a wastewater treatment plant. The method has advantages in improving data stability and providing better help for decision makers compared to the linear algorithm. The theoretical proof and simulation verification demonstrate the effectiveness and feasibility of this method, which can guide the design and operation of sewage treatment plants.