论文标题
在预测东南亚严重雾霾的情况下利用深度学习
Exploiting deep learning in forecasting the occurrence of severe haze in Southeast Asia
论文作者
论文摘要
在东南亚，由颗粒污染引起的严重雾霾或低可见性事件已成为严重的环境问题。已经开发了基于深卷积神经网络的此类事件的预测框架。在过去35年中，使用多达18个气象和水文变量的时间顺序图与表面可见性数据一起训练该框架。在新加坡的预测雾霾与无与伦比的情况下，受过训练的机器达到了良好的总体准确性，可以轻松地超过基于雾霾出现频率的无技能预测。但是，该机器仍会产生相对较大的丢失预测（对雾事件的假阴性），这可能是由于其在识别非典型模式方面缺乏经验。然而，这项工作表明了使用深度学习算法预测极端环境和天气事件的发生，并促进有关这些仍然鲜为人知的现象的知识的前景。
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.
