Monitoring Atmospheric Pollutants From Ground-based Observations
Author:
Funder
European Regional Development Fund
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9703441/9703388/09703595.pdf?arnumber=9703595
Reference8 articles.
1. Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing
2. A Data-Driven Approach for Accurate Rainfall Prediction
3. Development of a daily PM<sub>10</sub> and PM<sub>2.5</sub> prediction system using a deep long short-term memory neural network model
4. Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks
5. Forecasting Precipitable Water Vapor Using LSTMs
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1. CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
2. BiLSTM−BiGRU: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
3. NeSDeepNet: A Fusion Framework for Multi-step Forecasting of Near-surface Air Pollutants;2023 Photonics & Electromagnetics Research Symposium (PIERS);2023-07-03
4. A Data-Driven Analysis of Formula 1 Car Races Outcome;Communications in Computer and Information Science;2023
5. Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin;Remote Sensing Applications: Society and Environment;2022-11
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