An empirical study of PM2.5 forecasting using neural network-Reference-Cited by-同舟云学术

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An empirical study of PM2.5 forecasting using neural network

Published:2017-08 Issue: Volume: Page:
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Container-title:2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
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Author:

Mahajan Sachit,Chen Ling-Jyh,Tsai Tzu-Chieh

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8386711/8397387/08397443.pdf?arnumber=8397443

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analysis and prediction of PM2.5 in Medellin based on seasonal time series*;2023 IEEE Colombian Caribbean Conference (C3);2023-11-22

2. Deep transition network with gating mechanism for multivariate time series forecasting;Applied Intelligence;2023-07-23

3. Human migration-based graph convolutional network for PM2.5 forecasting in post-COVID-19 pandemic age;Neural Computing and Applications;2022-11-22

4. Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks;Applied Sciences;2022-11-08

5. Using Ensemble Machine Learning Methods to Forecast Particulate Matter (PM2.5) in Bangkok, Thailand;Lecture Notes in Computer Science;2022

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