论文标题
有条件的生成对抗网络，以建模城市户外空气污染
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution
论文作者
论文摘要
这是一个相关的问题，因为大多数城市的设计优先考虑使用机动车辆，该车辆近年来已经退化了空气质量，对城市健康产生了负面影响。建模，预测和预测环境空气污染是解决此问题的重要方法，因为这对决策者和城市城市计划者来说是有帮助的，可以理解现象并采取解决方案。通常，用于建模，预测和预测室外污染的数据驱动方法需要大量数据，这可能会限制其准确性。为了处理这种缺乏数据，我们建议训练能够根据给定的分类生成合成二氧化氮日期序列的模型，该分类将允许无限生成的现实数据。主要实验结果表明，所提出的方法能够产生准确而多样的污染日常时间序列，同时需要减少计算时间。
This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient air pollution is an important way to deal with this issue because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven methods for modeling, predicting, and forecasting outdoor pollution requires an important amount of data, which may limit their accuracy. In order to deal with such a lack of data, we propose to train models able to generate synthetic nitrogen dioxide daily time series according to a given classification that will allow an unlimited generation of realistic data. The main experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.
