Real-time Air Pollution prediction model based on Spatiotemporal Big data
This addresses air pollution monitoring for urban planners and public health, but it is incremental as it combines existing methods (CNN and LSTM) on new sensor data.
The paper tackles real-time air pollution prediction in urban areas using spatiotemporal big data from sensors on taxis, proposing a hybrid CNN-LSTM model that achieves good prediction ability.
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.