A real-time hourly ozone prediction system using deep convolutional neural network

TL;DR

This paper presents a deep convolutional neural network approach for real-time hourly ozone prediction in Seoul, demonstrating reasonable accuracy but highlighting limitations in peak prediction and nighttime forecasting.

Contribution

The study introduces a CNN-based model for hourly ozone forecasting using multiple meteorological and pollutant predictors, achieving fast predictions and analyzing spatial and temporal performance.

Findings

Average IOA of 0.84-0.89 across sites

Pearson correlation coefficient of 0.74-0.81

More accurate in southern regions and during daytime

Abstract

This study uses a deep learning approach to forecast ozone concentrations over Seoul, South Korea for 2017. We employ a deep convolutional neural network (CNN). We apply this method to predict the hourly ozone concentration on each day for the entire year using several predictors from the previous day, including the wind fields, temperature, relative humidity, pressure, and precipitation, along with in-situ ozone and NO2 concentrations. We refer to a history of all observed parameters between 2014 and 2016 for training the predictive models. Model-measurement comparisons for the 25 monitoring sites for the year 2017 report average indexes of agreement (IOA) of 0.84-0.89 and a Pearson correlation coefficient of 0.74-0.81, indicating reasonable performance for the CNN forecasting model. Although the CNN model successfully captures daily trends as well as yearly high and low variations of the ozone concentrations, it notably underpredicts high ozone peaks during the summer. The forecasting results are generally more accurate for the stations located in the southern regions of the Han River, the result of more stable topographical and meteorological conditions. Furthermore, through two separate daytime and nighttime forecasts, we find that the monthly IOA of the CNN model is 0.05-0.30 higher during the daytime, resulting from the unavailability of some of the input parameters during the nighttime. While the CNN model can predict the next 24 hours of ozone concentrations within less than a minute, we identify several limitations of deep learning models for real-time air quality forecasting for further improvement.