Hybrid interpretable predictive machine learning model for air pollution prediction

Air pollution prediction is a burning issue, as pollutants can harm human health. Traditional machine learning models usually aim to improve the overall prediction accuracy but neglect the accuracy for peak values. Moreover, these models are not interpretable. They fail to explain the interactions between var-ious determining factors and their impacts on air pollution. In this paper, we propose a new Hybrid Interpretable Predictive Machine Learning model for the Particulate Matter 2.5 prediction, which carries two novelties. First, a hybrid model structure is constructed with deep neural network and Nonlinear Auto Regressive Moving Average with Exogenous Input model. Second, automatic feature generation and feature selection procedures are integrated into this hybrid model. The experimental results demon-strate the superiority of our model over other models in prediction accuracy for peak values and model interpretability. The proposed model reveals how PM2.5 prediction is estimated by historical PM2.5, weather, and season. The accuracies (measured by correlation coefficients) of 1, 3 and 6-hour-ahead pre-diction are 0.9870, 0.9332 and 0.8587, respectively. More importantly, the proposed approach presents a new interpretable machine learning framework for time series data, enabling to explain complex depen-dence of multimode inputs, and to build reliable predictive models. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduces a new Hybrid Interpretable Predictive Machine Learning model for PM2.5 prediction, which outperforms other models in terms of prediction accuracy for peak values and model interpretability. The model effectively explains how PM2.5 prediction is estimated through factors such as historical data, weather, and season.