Prediction of $$\hbox {PM}_{2.5}$$ concentration based on weighted bagging and image contrast-sensitive features

Abstract

In this paper, we propose an effective method of $$\hbox {PM}_{2.5}$$ prediction based on image contrast-sensitive features and weighted bagging based neural network (WBBNN). Three types of image contrast-sensitive features are first extracted from the images and fuzzified. Next, a weighted bagging strategy combining the ensemble fuzzy neural network (FNN) and ensemble radial basis function neural network (RBFNN) is established. The ensemble neural network (NN), regardless of FNN and RBFNN, is obtained by simply averaging the outputs of component neural networks. And these component neural networks are trained by the improved gradient descent algorithm and samples acquired by bootstrap sampling. Finally, the WBBNN is used to forecast $$\hbox {PM}_{2.5}$$ concentration by extracting three types of image contrast-sensitive features. Results of experiments demonstrate that our prediction method is more reliable, practical and efficient than FNN, RBFNN, the ensemble NNs, and state-of-the-art quality assessment method in terms of predicting the concentration of $$\hbox {PM}_{2.5}$$. More importantly, an improved gradient descent algorithm is developed to accelerate the convergence speed and ensure the prediction accuracy of WBBNN and the fuzzy features acquired by the feature fuzzification method can greatly improve the robustness and precision of WBBNN.