来自杂志 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 的文献。
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1.
张浩彬
(2022-06-20 08:27):
#paper doi:10.1145/3219819.3219822，Deep Distributed Fusion Network for Air Quality Prediction
.2018年的kdd论文，从现在的角度看，或者从当时的角度看，作者所构建的这个网络都并不复杂。这个网络主要包括2个组件：空间变换组件以及深度分布融合组件。
1.设计了一个空间变换组件，将空间稀疏的空气质量数据转换为模拟二手污染物源的一致输入， 借助来自空间邻居的信号（根据分布方位分为远近及东西南北向供16个），DeepAir 在一般情况和突变情况下具有更好的性能。
2.考虑到直接和间接因素对空气质量的影响不同，分别使用每个间接因素与直接因素进行一个子网络构建，以及构建一个整体子网络，最后进行融合。。
3.这个论文的网络结构虽然不复杂，但是却很贴近业务。是基于业务的基础上对网络进行设计的。作者基于 9 个中国城市的三年数据，结果表明 DeepAir 与 10 个基线相比具有优势。 在短期、长期和突变预测方面的相对准确度分别提高了 2.4%、12.2%、63.2%。
Abstract:
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 …
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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.
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