A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

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1. Development of a Conv-LSTM-Based AI Model for Forecasting PM2.5 Spatial Concentration Distribution M. Jung et al. 10.5572/KOSAE.2025.41.1.067
2. Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM D. Li et al. 10.3390/atmos13101719
3. Nonlinear relationships of commuting and built environments surrounding residences and workplaces with obesity C. Yin et al. 10.1057/s41599-024-03964-9
4. Stock Price Prediction Using Deep-Learning Models: CNN, RNN, and LSTM R. Cao & D. Bassir 10.1051/shsconf/202419602004
5. An optimized decomposition integration model for deterministic and probabilistic air pollutant concentration prediction considering influencing factors F. Yang & G. Huang 10.1016/j.apr.2024.102144
6. A graph neural networks approach predicted spatiotemporal changes of ozone concentrations in the Yangtze River Delta (China) Z. Lin et al. 10.1088/2515-7620/ae0012
7. Multi-Horizon Air Pollution Forecasting with Deep Neural Networks M. Arsov et al. 10.3390/s21041235
8. Long-Term Prediction of PM2.5 Concentration Based on CNN-XLSTM and Spatial Attention Mechanism 棋. 刘 10.12677/sea.2025.142033
9. Uzun-Kısa Süreli Bellek Ağı Kullanarak Hisse Senedi Fiyatı Tahmini M. TOKMAK 10.31200/makuubd.1164099
10. Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities S. Kumar et al. 10.1109/ACCESS.2025.3546504
11. Analysis of deep learning approaches for air pollution prediction V. Gugnani & R. Singh 10.1007/s11042-021-11734-x
12. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models T. Xayasouk et al. 10.3390/su12062570
13. A novel bidirectional DiPLS based LSTM algorithm and its application in industrial process time series prediction Y. Wang et al. 10.1016/j.chemolab.2023.104878
14. Überwachung der Sedierung in der Endoskopie mit künstlicher Intelligenz J. Garbe et al. 10.1007/s11377-020-00494-z
15. A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration D. Qin et al. 10.1109/ACCESS.2019.2897028
16. Deep Learning for Air Quality Forecasts: a Review Q. Liao et al. 10.1007/s40726-020-00159-z
17. Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
18. Environmental Pollution Analysis and Impact Study—A Case Study for the Salton Sea in California J. Gao et al. 10.3390/atmos13060914
19. A feature selection and multi-model fusion-based approach of predicting air quality Y. Zhang et al. 10.1016/j.isatra.2019.11.023
20. A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology S. Weichenthal et al. 10.1016/j.envint.2018.11.042
21. Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India M. Krishan et al. 10.1007/s11869-019-00696-7
22. iDetect for vulnerability detection in internet of things operating systems using machine learning A. Al-Boghdady et al. 10.1038/s41598-022-21325-x
23. A hybrid CNN-Transformer model for ozone concentration prediction Y. Chen et al. 10.1007/s11869-022-01197-w
24. Transforming air pollution management in India with AI and machine learning technologies K. Rautela & M. Goyal 10.1038/s41598-024-71269-7
25. MapLUR M. Steininger et al. 10.1145/3380973
26. Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks B. Peralta et al. 10.3390/app122211317
27. A Complete Air Pollution Monitoring and Prediction Framework J. Kalajdjieski et al. 10.1109/ACCESS.2023.3251346
28. Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters M. Kubara 10.1007/s00168-023-01220-7
29. Deep learning PM2.5 concentrations with bidirectional LSTM RNN W. Tong et al. 10.1007/s11869-018-0647-4
30. BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer A. Verma et al. 10.1007/s10661-024-12455-y
31. Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method Z. Ye et al. 10.1051/e3sconf/201913605001
32. Forecasting air quality time series using deep learning B. Freeman et al. 10.1080/10962247.2018.1459956
33. Deep learning for ocean temperature forecasting: a survey X. Zhao et al. 10.1007/s44295-024-00042-3
34. Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea J. Yeom et al. 10.1088/1748-9326/ab9467
35. A multi-task stations cooperative air quality prediction system for sustainable development B. Li & P. Wang 10.1057/s41599-024-03532-1
36. Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability Z. Wang et al. 10.1016/j.isprsjprs.2022.03.002
37. Multimodal Imputation-Based Multimodal Autoencoder Framework for AQI Classification and Prediction of Indian Cities R. Srinivasa Rao et al. 10.1109/ACCESS.2024.3438573
38. A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting M. Zulqarnain et al. 10.32604/cmc.2022.021629
39. Analysis of the Main Anthropogenic Sources’ Contribution to Pollutant Emissions in the Lazio Region, Italy G. Battista et al. 10.3390/app11177936
40. Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks J. Kalajdjieski et al. 10.3390/rs12244142
41. Machine learning for observation bias correction with application to dust storm data assimilation J. Jin et al. 10.5194/acp-19-10009-2019
42. Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework M. Ahmed Hamza et al. 10.32604/cmc.2022.029604
43. Deep learning methods for atmospheric PM2.5 prediction: A comparative study of transformer and CNN-LSTM-attention B. Cui et al. 10.1016/j.apr.2023.101833
44. Temporally boosting neural network for improving dynamic prediction of PM2.5 concentration with changing and unbalanced distribution H. Shi et al. 10.1016/j.jenvman.2025.125371
45. A Hybrid Autoformer Network for Air Pollution Forecasting Based on External Factor Optimization K. Pan et al. 10.3390/atmos14050869
46. A time series forecasting based multi-criteria methodology for air quality prediction R. Espinosa et al. 10.1016/j.asoc.2021.107850
47. A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction B. Zhang et al. 10.1016/j.scitotenv.2020.144507
48. Überwachung der Sedierung in der Endoskopie mit künstlicher Intelligenz J. Garbe et al. 10.1007/s00740-021-00424-x
49. Improving air pollution detection accuracy and quality monitoring based on bidirectional RNN and the Internet of Things D. Saravanan & K. Santhosh Kumar 10.1016/j.matpr.2021.04.239
50. A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance A. Masood & K. Ahmad 10.1016/j.jclepro.2021.129072
51. Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM J. Ma et al. 10.1109/ACCESS.2019.2932445
52. DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting S. Fang et al. 10.1007/s11356-022-19574-4
53. A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network G. Huang et al. 10.1007/s11356-022-22454-6
54. Exploring deep learning for air pollutant emission estimation L. Huang et al. 10.5194/gmd-14-4641-2021
55. Multi-Site and Multi-Pollutant Air Quality Data Modeling M. Hu et al. 10.3390/su16010165
56. A review of Earth Artificial Intelligence Z. Sun et al. 10.1016/j.cageo.2022.105034
57. Modeling PM2.5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm J. Gregório et al. 10.3390/atmos13081334
58. A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST A. Verma et al. 10.1007/s10661-023-12020-z
59. Dioxin emission prediction from a full-scale municipal solid waste incinerator: Deep learning model in time-series input C. Wen et al. 10.1016/j.wasman.2023.08.004
60. Long-Term Air Quality Evaluation System Prediction In China Based On Multinomial Logistic Regression Method Y. He et al. 10.24057/2071-9388-2023-2719
61. An air quality prediction model based on improved Vanilla LSTM with multichannel input and multiroute output W. Fang et al. 10.1016/j.eswa.2022.118422
62. Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter S. Lee & J. Shin 10.18178/IJIEE.2019.9.1.701
63. Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors T. Do et al. 10.1109/JIOT.2020.2999446
64. Air quality prediction using CT-LSTM J. Wang et al. 10.1007/s00521-020-05535-w
65. Knowledge discovery from remote sensing images: A review L. Wang et al. 10.1002/widm.1371
66. PM2.5 forecasting under distribution shift: A graph learning approach Y. Liu et al. 10.1016/j.aiopen.2023.11.001
67. On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method Z. Ren & X. Ji 10.1016/j.apr.2023.101731
68. High return and low risk: Shaping composite financial investment decision in the new energy stock market Q. Zhu et al. 10.1016/j.eneco.2023.106683
69. A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction S. Tsokov et al. 10.3390/su14095104
70. Resilience to Air Pollution: A Novel Approach for Detecting and Predicting Aerosol Atmospheric Rivers within Earth System Boundaries K. Rautela et al. 10.1007/s41748-024-00421-0
71. Graph Neural Network for Air Quality Prediction: A Case Study in Madrid D. Iskandaryan et al. 10.1109/ACCESS.2023.3234214
72. Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review F. Bahadur et al. 10.1007/s10661-023-12001-2
73. Regional air quality forecasting using spatiotemporal deep learning S. Abirami & P. Chitra 10.1016/j.jclepro.2020.125341
74. Hybrid short-term traffic flow prediction based on the effect of non-linear sequence noise G. Cheng & Y. Liu 10.3934/era.2024034
75. Forecasting upper atmospheric scalars advection using deep learning: an $$O_3$$ experiment L. Steffenel et al. 10.1007/s10994-020-05944-x
76. The Identification and Prediction in Abundance Variation of Atlantic Cod via Long Short-Term Memory With Periodicity, Time–Frequency Co-movement, and Lead-Lag Effect Across Sea Surface Temperature, Sea Surface Salinity, Catches, and Prey Biomass From 1919 to 2016 R. Nian et al. 10.3389/fmars.2021.665716
77. Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito P. Chau et al. 10.3389/fdata.2022.842455
78. A Hybrid Deep Learning Model for Multi-step Ahead Prediction of PM2.5 Concentration Across India P. Goswami et al. 10.1007/s10666-023-09902-4
79. Hyperparameter-Optimization-Inspired Long Short-Term Memory Network for Air Quality Grade Prediction D. Wen et al. 10.3390/info14040243
80. A forecasting framework on fusion of spatiotemporal features for multi-station PM2.5 J. Wang et al. 10.1016/j.eswa.2023.121951
81. A Methodological Comparison on Spatiotemporal Prediction of Criteria Air Pollutants P. Singh et al. 10.5572/ajae.2021.087
82. Regional PM2.5 prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors Y. Chen et al. 10.1016/j.envpol.2024.125404