首页|Studies from Nanjing University of Information Science and Technology (NUIST) Yield New Information about Machine Learning (Improved Prediction of Hourly Pm2.5 Concentrations With a Long Short-term Memory and Spatio-temporal Causal ...)
Studies from Nanjing University of Information Science and Technology (NUIST) Yield New Information about Machine Learning (Improved Prediction of Hourly Pm2.5 Concentrations With a Long Short-term Memory and Spatio-temporal Causal ...)
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NETL
NSTL
Elsevier
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Nanjing, People's Republic of China, by NewsRx correspondents, research stated, "Accurate prediction of particulate matter with aerodynamic diameter ＜= 2.5 mu m (PM2.5) is important for envi-ronmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the Nanjing University of Information Science and Technology (NUIST), "In this study, we propose a deep learning model for short-term PM2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial corre-lation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant in-formation is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM2.5 concentrations for the next hour. The experimental results show that the predicted PM2.5 concentrations are consistent with observation, with correlation coefficient (R2) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 mu g/m3. Additionally, to better un-derstand the factors affecting PM2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM2.5 and its influencing factors. By identifying the main factors affecting PM2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE)."
NanjingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRisk and PreventionNanjing University of Information Science and Technology (NUIST)
NETL
NSTL
Elsevier
