A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Alqamah Sayeed¹ㆍYunsoo Choi¹ㆍEbrahim Eslami¹ㆍJia Jung¹ㆍYannic Lops¹ㆍAhmed Salman¹ㆍJae-Bum Lee²ㆍHyun-Ju Park²ㆍMin-Hyeok Choi²

¹Department of Earth and Atmospheric Sciences, University of Houston
²National Institute of Environmental Research, Incheon, Korea

A Cluster-based Graph Neural Network Approach for Forecasting PM2.5 Concentrations in India using Monitoring Sensors Data

Ejurothu Pavan Sai SanthoshㆍSubhojit MandalㆍMainak Thakur

Department of Computer Science and Engineering, Indian Institute of Information Technology, Sricity

Determination of Input variables for Artificial Intelligence Models to predict the High PM2.5 concentration events in Seoul

Moon-Soo ParkㆍSang-Heon Kim¹

Department of Climate and Environment, Sejong University
¹Climate Change & Environmental Research Center, Sejong University

LSTM-based PM2.5 forecast system in Korea

Ho, Chang-Hoi¹ㆍIngyu Park1ㆍJinwon Kim²ㆍJae-Bum Lee³

¹School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea
²National Institute of Meteorological Sciences, Seogwipo, Republic of Korea
³National Institute of Environmental Research, Incheon, Republic of Korea

Long-Term Air Quality Prediction of South Korea Based on Deep Neural Network

Sangkyun Lee

School of Cybersecurity, Korea University

Virtual observation at green and red bands of geostationary environment monitoring spectrometer using a deep learning technique

Han-Sol Ryu¹ㆍJeong-Eun Park1ㆍGoo Kim²ㆍJaehoon Jeong²ㆍSungwook Hong¹

¹Department of Environment, Energy, and Geoinformatics, Sejong University
²Environmental Satellite Center, National Institute of Environmental Research