A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM

TL;DR

This paper presents a deep learning approach using LSTM-based RNNs and encoder-decoder models to forecast air pollution levels in South Korea and other Asian cities, aiming to improve prediction accuracy for better policy-making.

Contribution

It introduces a novel application of LSTM RNNs with encoder-decoder architecture for air pollution forecasting, evaluating various configurations for improved long-term predictions.

Findings

Multi-layer RNNs do not significantly improve far-term forecast accuracy.

The model effectively captures temporal patterns in air pollution data.

Forecasting performance varies with different neural network configurations.

Abstract

Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental science to evaluate the dangerous impact of particulate matters on public health. Besides that, deterministic models of air pollutant behavior are also generated; however, this is both complex and often inaccurate. On the contrary, deep recurrent neural network reveals potent potential on forecasting out-comes of time-series data and has become more prevalent. This paper uses Recurrent Neural Network (RNN) with Long Short-Term Memory units as a framework for leveraging knowledge from time-series data of air pollution and meteorological information in Daegu, Seoul, Beijing, and Shenyang. Additionally, we use encoder-decoder model, which is similar to machine comprehension problems, as a crucial part of our prediction machine. Finally, we investigate the prediction accuracy of various configurations. Our experiments prevent the efficiency of integrating multiple layers of RNN on prediction model when forecasting far timesteps ahead. This research is a significant motivation for not only continuing researching on urban air quality but also help the government leverage that insight to enact beneficial policies