Deep Distributed Fusion Network for Air Quality Prediction
By Xiuwen Yi, Junbo Zhang, Zhaoyuan Wang, Tianrui Li, Yu Zheng. Knowledge Discovery and Data Mining, 2018
328 citations recorded by the source corpus.
Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and people's decision making. In this paper, we predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data. Based on the domain knowledge about air pollution, we propose a deep neural network (DNN)-based approach (entitled DeepAir), which consists of a spatial transformation component and a deep distributed fusion network. Considering air pollutants' spatial correlations, the former component converts the spatial sparse air quality data into a consistent input to simulate the pollutant sources. The latter network adopts a neural distributed architecture to fuse heterogeneous urban data for simultaneously capturing the factors affecting air quality, e.g. meteorological conditions. We deployed DeepAir in our AirPollutionPrediction system, providing fine-grained air quality forecasts for 300+ Chinese cities every hour. The experimental results on the data from three-year nine Chinese-city demonstrate the advantages of DeepAir beyond 10 baseline methods. Comparing with the previous online approach in AirPollutionPrediction system, we have 2.4%, 12.2%, 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively.
Subfields: Environmental Engineering, Global and Planetary Change, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/9f7fa6b2da4c002925421893037cbb2629aaf72c
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In Environmental Engineering
In Global and Planetary Change
In Health, Toxicology and Mutagenesis
Citations in the corpus
- Deep Air Quality Forecasting Using Hybrid Deep Learning Framework (2018, background): “A large number of air quality forecasting methods based on the big data have been proposed to help air pollution warning and control [37].”
- Towards Physics-informed Deep Learning for Turbulent Flow Prediction (2019, background): “a mining, most work on spatiotemporal forecasting has been purely data-driven where complex deep learning models are learned directly from data without explicitly enforcing physical constraints, e.g. [16, 40, 43, 44]. A few recent works [1, 12] tried to incorporate physical knowledge into deep learn”