# Real-time Air Pollution prediction model based on Spatiotemporal Big data

arXiv: https://arxiv.org/abs/1805.00432 · HTML: https://mlsanity.com/paper/1805.00432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM](https://mlsanity.com/paper/2211.09814/) (2022)

## Abstract (arXiv)

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.