The identification and control of human factors in climate change is a rapidly growing concern and robust, real-time air-quality monitoring and forecasting plays a critical role in allowing effective policy formulation and implementation. This paper presents DELFI, a novel deep learning-based mixture model to make effective long-term predictions of Particulate Matter (PM) 2.5 concentrations. A key novelty in DELFI is its multi-scale approach to the forecasting problem. The observation that point predictions are more suitable in the short-term and probabilistic predictions in the long-term allows accurate predictions to be made as much as 24 hours in advance. DELFI incorporates meteorological data as well as pollutant-based features to ensure a robust model that is divided into two parts: (i) a stack of three Long Short-Term Memory (LSTM) networks that perform differential modelling of the same window of past data, and (ii) a fully-connected layer enabling attention to each of the components. Experimental evaluation based on deployment of 13 stations in the Delhi National Capital Region (Delhi-NCR) in India establishes that DELFI offers far superior predictions especially in the long-term as compared to even non-parametric baselines. The Delhi-NCR recorded the 3rd highest PM levels amongst 39 mega-cities across the world during 2011-2015 and DELFI's performance establishes it as a potential tool for effective long-term forecasting of PM levels to enable public health management and environment protection.
翻译:确定和控制气候变化中的人类因素是一个迅速增长的关注问题,可靠、实时的空气质量监测和预报在有效制定和执行政策方面发挥着关键作用。本文件介绍了DELFI,这是一个全新的深层次学习混合模型,这是对片状物质(PM)2.5浓度进行有效长期预测的新颖做法。DELFI的一个重要新颖之处是其应对预测问题的多尺度方法。观察到点预测更适合短期预测和长期概率预测,因此可以提前24小时作出准确预测。DELFI包含气象数据和污染物特征,以确保一个强有力的模型,该模型分为两个部分:(一) 一组三个长期短期记忆(LSTM)网络,对以往数据的同一窗口进行不同的建模;(二) 一个完全相连的层,能够关注每个组成部分。根据在德里国家首都区部署的13个站进行实验性评估,印度的DELFI提供了远优的预测,特别是在39-2015年中期基准和最低基准水平上,在2011-2015年全球基准和最低基准水平上,甚至在整个基准水平上,为基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-全球水平-全球水平-全球记录-全球记录-全球记录-全球记录,在非基准-基准-基准-基准-基准-基准-全球记录-基准-基准-基准-基准-基准-基准-基准-基准-全球记录-全球记录-全球记录-基准-基准-基准-基准-基准-基准-基准-基准-全球水平-全球水平-全球水平,整个三,整个三,整个三-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-基准-