Improved prediction model for daily PM concentrations with particle swarm optimization and BP neural network.

With the acceleration of urbanization in China, haze pollution has become a problem that cannot be ignored. PM is one of the main components of haze, and this paper aims to find a stable and accurate prediction method for PM prediction. Combined with existing studies, BP neural network is commonly used for prediction and optimization, but its accuracy is not satisfactory due to the randomness of the initial parameters of BP neural network. In order to solve this problem, this study proposes a new type of fusion model-improved particle swarm optimized backpropagation neural network (IPSO-BP) model. In this paper, we use the BP neural network to predict the value of PM, and at the same time, we use the improved particle swarm algorithm to optimize the initial parameters of the BP neural network, which makes the prediction performance improved. Taking a simulation experiment in Nanchang City as an example, the prediction accuracy is 86.76%, the correlation coefficient R is 0.95734, and the root-mean-square error (RMSE) is 5.2407. Compared with a single BP neural network model, the advantages of the IPSO-BP model are: (1) Asynchronous learning factor is used, particle swarm algorithm (PSO) exists two learning factors, individual learning factor c1 and population learning factor c2, the former affects the local search ability while the latter affects the global search ability. Through the iterative formula proposed in this paper, the algorithm can be made to satisfy the strong global search ability in the early stage and the strong local search ability in the later stage. (2) Adaptive inertia weights are introduced, where larger values of inertia weights mean that it is more difficult to change the direction of the particles. In the initial stage of the model, a larger inertia weight helps to improve the global search ability of the algorithm, while a smaller inertia weight helps to improve the local search ability of the algorithm as it enters the end of the search. Adaptive inertia weights are the iterative formulas proposed in the paper that make the inertia weights of the model large at the beginning and small at the end. (3) Incorporating the Levy flight search strategy, which aims to solve the shortcomings of traditional particle swarm algorithms that often fall into the suboptimal solution, it can be judged according to the evolutionary effect of the particle position, and if the particles are still unable to enter the more optimal position in many iterations, the Levy flight will be used to update the position of the particles, which is a strategy that increases the vitality of the particles. In summary, the IPSO-BP model proposed in this study has excellent predictive ability and, makes some positive contributions to the cause of air pollution prevention.