利用深度学习预测东南亚严重雾霾的发生
Exploiting deep learning in forecasting the occurrence of severe haze in Southeast Asia
摘要：
由颗粒物污染引起的严重霾或低能见度事件已成为东南亚地区严重的环境问题。已经开发了基于深度卷积神经网络的此类事件的预测框架。在过去35年中，该框架使用了多达18个气象和水文变量的时序图以及地表能见度数据进行了培训。在预测新加坡的雾霾与无雾霾情况时，经过训练的机器已经实现了良好的整体准确性，很容易超过基于雾霾发生频率的无技能盲预测。然而，该机器仍然产生了相对较高数量的遗漏预报（雾霾事件的假阴性），这可能是由于它缺乏识别非典型模式的经验。尽管如此，这一努力已经证明了使用深度学习算法来预测极端环境和天气事件的发生，并推进对这些仍然知之甚少的现象的了解的前景。
英文摘要：
Severe haze or low visibility event caused by particulate pollution has become a serious environmental issue in Southeast Asia. A forecasting framework of such events based on deep convolutional neural networks has been developed. The framework has been trained using time sequential maps of up to 18 meteorological and hydrological variables alongside surface visibility data over past 35 years. In forecasting haze versus no-haze situations in Singapore, the trained machine has achieved a good overall accuracy that easily exceeds that of the no-skill blinded forecast based on haze occurrence frequency. However, the machine still produces a relatively high number of missing forecasts (false negative for haze events), likely owing to its lack of experience in identifying atypical patterns. Nevertheless, this effort has demonstrated a promising prospect of using deep learning algorithms to predict the occurrence of extreme environmental and weather events, and to advance knowledge about these still poorly known phenomena.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
