Urban Ecological Monitoring and Prediction Based on Deep Learning

Abstract

Obtaining comprehensive and accurate air quality information is conducive to people’s daily travel and living arrangements, especially to protect people’s health from air pollutants. Due to the limited number of air quality monitoring stations and the lack of training samples, the generalisation performance of air quality estimation model is often not good enough. Therefore, we propose an urban air quality index (AQI) prediction and AQI level estimation method based on deep multi-task learning. We consider various urban big data information related to air quality (meteorology, transportation, enterprise self-test, POI, road network, etc.), and use machine learning methods such as deep learning and graph embedding learning to learn the representation of relevant information, and establish the relationship between these related representations and air quality. Experiments show that this scheme can estimate the level of urban air quality index joint prediction task and air quality index, and the model has generalisation performance.