Developments in deep learning for time-series problems have shown promising results for data prediction. Particulate Matter equal or smaller than 10 μm (PM₁₀) have increased importance in the research field due to the negative impact in the respiratory system. PM₁₀ particles show non-linear behavior, hence it is not an easy task to implement techniques to predict subsequent concentration of the particles in the atmosphere. This paper presents a forecasting model using gated Recurrent unit (GRU) and Long-Short Term Memory (LSTM) networks, which are types of a deep recurrent neural network (RNN). The predicted results of PM₁₀ are presented using data of Mexico City as a case study, showing that this type of deep network is feasible for predicting the non-linearities of this type of data. Several experiments were carried out for 12, 24, 48, and 120 h prediction, showing that this method may be applied to accurately forecast the behavior of PM10.

中文翻译：

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基于门控递归单元（GRU）深度神经网络的机载颗粒物污染预测模型

针对时间序列问题的深度学习方面的发展已显示出可预测的数据预测结果。由于对呼吸系统的负面影响，等于或小于10μm（PM ₁₀）的颗粒物在研究领域的重要性日益提高。PM ₁₀颗粒表现出非线性行为，因此要实施预测大气中颗粒随后浓度的技术并非易事。本文提出了使用门控递归单元（GRU）和长短期记忆（LSTM）网络的预测模型，这是一种深度递归神经网络（RNN）。PM ₁₀的预测结果本文以墨西哥城的数据为例进行了介绍，表明这种深度网络对于预测此类数据的非线性是可行的。进行了12、24、48和120 h预测的几个实验，表明该方法可用于准确预测PM10的行为。