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Forecasting air quality time series using deep learning

机译：使用深度学习预测空气质量时间序列

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This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O_3) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O_3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.
机译：本文介绍了深度学习（DL）技术预测空气污染时间序列的最早应用之一。空气质量管理在很大程度上依赖于在空气监测站捕获的时间序列数据，以此作为确定人群暴露在空气中的污染物并确定是否符合当地环境空气标准的基础。在本文中，使用深度学习预测了8小时的平均表面臭氧（O_3）浓度，该深度学习由具有长期短期记忆（LSTM）的递归神经网络（RNN）组成。每小时的空气质量和气象数据被用来训练和预测长达72小时的值，且错误率较低。 LSTM还能够预测连续O_3超出的持续时间。在训练网络之前，对数据集进行了检查，以查找缺失的数据和异常值。丢失的数据是使用一种新技术估算的，该技术根据相邻时间段的一阶差异，平均间隔少于八个时间步长的增量为增量。然后，将数据用于训练决策树，以评估不同时间预测范围内输入特征的重要性。用于训练LSTM模型的特征数量从25个特征减少到5个特征，从而通过平均绝对误差（MAE）进行了测量，从而提高了准确性。参数敏感性分析确定了与RNN相关的回溯节点，如果与预测范围不一致，则被证明是错误的重要来源。总体而言，MAE小于2是针对72小时的预测计算得出的。启示：新颖的深度学习技术被用来训练平均8小时的臭氧预报模型。使用新的插补方法替换了捕获数据集中的缺失数据和离群值，该插补方法根据时间和季节生成的计算值更接近于预期值。决策树用于识别最重要的输入变量。本文介绍的方法使空气管理人员可以预测远程空气污染浓度，同时仅监视关键参数，而无需完全转换数据集，从而可以进行实时输入和连续预测。

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    Brian S. Freeman; Graham Taylor; Bahram Gharabaghi; Jesse The;

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    School of Engineering, University of Guelph, Guelph, Ontario, Canada;

    School of Engineering, University of Guelph, Guelph, Ontario, Canada;

    School of Engineering, University of Guelph, Guelph, Ontario, Canada;

    School of Engineering, University of Guelph, Guelph, Ontario, Canada,Lakes Environmental, Waterloo, Ontario, Canada;

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