Air pollution is one of the most concerns for urban areas. Many countries have constructed monitoring stations to hourly collect pollution values. Recently, there is a research in Daegu city, Korea for real-time air quality monitoring via sensors installed on taxis running across the whole city. The collected data is huge (1-second interval) and in both Spatial and Temporal format. In this paper, based on this spatiotemporal Big data, we propose a real-time air pollution prediction model based on Convolutional Neural Network (CNN) algorithm for image-like Spatial distribution of air pollution. Regarding to Temporal information in the data, we introduce a combination of a Long Short-Term Memory (LSTM) unit for time series data and a Neural Network model for other air pollution impact factors such as weather conditions to build a hybrid prediction model. This model is simple in architecture but still brings good prediction ability.
@article{arxiv.1805.00432,
title = {Real-time Air Pollution prediction model based on Spatiotemporal Big data},
author = {Van-Duc Le and Tien-Cuong Bui and Sang Kyun Cha},
journal= {arXiv preprint arXiv:1805.00432},
year = {2025}
}
Comments
- We fix typos and grammars through out the paper. - We fix layout of figure for better view. - We update mathematic formula and its description. - We add more insights to experimental results. - We correct author names in references