A new IOIF Elman neural network for air quality prediction

The non-linearity and uncertainty of air pollutant data add a certain degree of difficulty to the prediction of air quality, and neural networks have a certain ability to solve this problem. But it has the shortcomings of slow convergence and is easy to fall into local minimums. Therefore, this paper optimizes the OIF Elman neural network (IOIF Elman) by the method of adaptive learning rate and direct input to output connections. And an air quality prediction model is established based on the IOIF Elman neural network. The simulation results show that the absolute average error (AAE) and accuracy of the IOIF Elman air quality prediction model are better than those of the OIF Elman and Elman neural network. The establishment of this model can better meet the needs of practical applications and provide a more reliable basis for decision-making for environmental protection departments.