A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea
A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea
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DOI：
10.3390/app10113984
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发表时间：
2020-06-01
影响因子：
2.7
通讯作者：
Jeon, Moongu
中科院分区：
文献类型：
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作者：
Qadeer, Khaula;Rehman, Wajih Ur;Jeon, Moongu
空气污染不仅破坏环境，还导致各种疾病，如呼吸道和心血管疾病。现在，估算空气污染物浓度变得非常重要，以便人们能够预先为空气污染的危害做好准备。各种确定性模型已被用于预测空气污染。在本研究中，沿着各种污染物和气象参数，我们还使用社区多尺度空气质量模式（CMAQ）预测的污染物浓度，这是强相关的PM2.5浓度。在结合这些参数后，我们实现了各种机器学习模型来预测韩国两个大城市的PM2.5浓度的小时预报，并比较了它们的结果。事实证明，长短期记忆网络优于其他著名的梯度树增强模型、递归和卷积神经网络。
Air pollution not only damages the environment but also leads to various illnesses such as respiratory tract and cardiovascular diseases. Nowadays, estimating air pollutants concentration is becoming very important so that people can prepare themselves for the hazardous impact of air pollution beforehand. Various deterministic models have been used to forecast air pollution. In this study, along with various pollutants and meteorological parameters, we also use the concentration of the pollutants predicted by the community multiscale air quality (CMAQ) model which are strongly related to PM2.5 concentration. After combining these parameters, we implement various machine learning models to predict the hourly forecast of PM2.5 concentration in two big cities of South Korea and compare their results. It has been shown that Long Short Term Memory network outperforms other well-known gradient tree boosting models, recurrent, and convolutional neural networks.