Improved Cloud-NARX Estimation Algorithm for Uncertainty Analysis of Air Pollution Prediction

Abstract

Air pollution causes significant negative impacts on climate, environment and human health. Monitoring and forecasting the PM2.5, one of the most dangerous pollutant, are crucial. However, the strong prediction uncertainty in peak periods can potentially increase the prediction error and decrease the model reliability. To overcome this problem, prediction intervals are needed to quantify the uncertainty and provide information of the confidence in the prediction. In this article, an improved cloud-NARX estimation algorithm is developed to quantify the uncertainty and produce prediction intervals. The proposed method integrates a new recursive estimation procedure and two new criteria, which significantly improve the training speed and prediction interval accuracy. The proposed method is applied to predict PM2.5 for one hour ahead. From our results, the proposed method achieves higher accuracies of both average predictions and prediction intervals than other methods. This study provides a novel framework for quantifying the uncertainty of time series prediction, and to improve the model robustness.