Accurate prediction of NOx emissions under actual working conditions is crucial for managing regional pollutant emissions.Therefore,this study focuses on agricultural tractors and employs a Portable Emission Measurement System(PEMS)to gather NOx emission under real operating conditions.By conducting correlation analyses of factors affecting NOx emissions,the main factors affecting NOx emissions during the actual working conditions of tractors were determined,and the NOx emission prediction model was established by applying these factors.In the process of establishing a NOx emission prediction model for agricultural tractors,the research utilizes the Back Propagation(BP)neural network,and the Long Short-Term Memory(LSTM)neural network,and optimizes both the BP and LSTM neural networks using Genetic Algorithm(GA)for comparison and evaluation of their prediction performance.The results demonstrate that among the established models,the opti-mized GA-BP neural network model excels in predicting NOx emissions.This model outperforms other neural network models in various evaluation metrics,including Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percent-age Error(MAPE),and the coefficient of determination(R2),with values of 5.679×10-3,4.057×10-3,3.751％and 0.991 5,respec-tively.Therefore,it is feasible to use the GA-BP neural network model to predict NO,emissions from agricultural tractors.
维普
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