A Prediction Model Based on Genetic Algorithm(GA)and Convolutional Neural Network(CNN)in Wastewater Treatment System
In this model that predicts effluent COD and SS in the processes of biological wastewater treatment,in order to solve the problem that a convolutional neural network does not follow rules regularly and it is difficult to guarantee the network to optimize,a convolutional neural network optimization method based on genetic algorithm is proposed.In this paper,the genetic algorithm(GA)and convolutional neural network(CNN)are combined to form a new hybrid algorithm-GA-CNN algorithm,and this new algorithm is compared with the CNN algorithm and BP neural networks on the aspect of prediction performance.The simulation results are as follows.For the concentration prediction of CODeff,the prediction performance of GA-CNN is 13.66％higher than that of CNN,which is 19.40％higher than BP,the prediction result of GA-CNN algorithm shows that the minimum root mean square error(RMSE)is 3.5305,the maximum correlation coefficient(R2)is 0.7195,and the minimum mean absolute percentage errors(MAPE)is 3.92％;for the concentration prediction of SSeff,the prediction performance of GA-CNN is 9.26％higher than that of CNN,which is 13.43％higher than BP,the prediction result of GA-CNN algorithm shows that the minimum root mean square error(RMSE)is 0.5883,the maximum correlation coefficient(R2)is 0.6770,and the minimum mean absolute percentage errors(MAPE)is 1.99％.
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