
|
19 Oct 2017
A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, and S. Lin
Viewed
Total article views: 3,018 (including HTML, PDF, and XML)
| HTML |
PDF |
XML |
Total |
BibTeX |
EndNote |
| 1,615 |
1,347 |
56 |
3,018 |
89 |
162 |
- HTML: 1,615
- PDF: 1,347
- XML: 56
- Total: 3,018
- BibTeX: 89
- EndNote: 162
Views and downloads (calculated since 19 Oct 2017)
Cumulative views and downloads
(calculated since 19 Oct 2017)
Viewed (geographical distribution)
Total article views: 2,864 (including HTML, PDF, and XML)
Thereof 2,851 with geography defined
and 13 with unknown origin.
|
| Total: |
0 |
| HTML: |
0 |
| PDF: |
0 |
| XML: |
0 |
Cited
82 citations as recorded by crossref.
-
Development of a Conv-LSTM-Based AI Model for Forecasting PM2.5 Spatial Concentration Distribution
M. Jung et al.
-
Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM
D. Li et al.
-
Nonlinear relationships of commuting and built environments surrounding residences and workplaces with obesity
C. Yin et al.
-
Spatiotemporal system forecasting with irregular time steps via masked autoencoder
K. Zhu et al.
-
Stock Price Prediction Using Deep-Learning Models: CNN, RNN, and LSTM
R. Cao & D. Bassir
-
An optimized decomposition integration model for deterministic and probabilistic air pollutant concentration prediction considering influencing factors
F. Yang & G. Huang
-
A graph neural networks approach predicted spatiotemporal changes of ozone concentrations in the Yangtze River Delta (China)
Z. Lin et al.
-
Multi-Horizon Air Pollution Forecasting with Deep Neural Networks
M. Arsov et al.
-
Long-Term Prediction of PM2.5 Concentration Based on CNN-XLSTM and Spatial Attention Mechanism
棋. 刘
-
Uzun-Kısa Süreli Bellek Ağı Kullanarak Hisse Senedi Fiyatı Tahmini
M. TOKMAK
-
Evaluating climate parameters over the Uttarakhand Himalaya in the context of climate change
S. Rawat et al.
-
Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities
S. Kumar et al.
-
Analysis of deep learning approaches for air pollution prediction
V. Gugnani & R. Singh
-
Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models
T. Xayasouk et al.
-
A novel bidirectional DiPLS based LSTM algorithm and its application in industrial process time series prediction
Y. Wang et al.
-
Überwachung der Sedierung in der Endoskopie mit künstlicher Intelligenz
J. Garbe et al.
-
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration
D. Qin et al.
-
Deep Learning for Air Quality Forecasts: a Review
Q. Liao et al.
-
Environmental Pollution Analysis and Impact Study—A Case Study for the Salton Sea in California
J. Gao et al.
-
A feature selection and multi-model fusion-based approach of predicting air quality
Y. Zhang et al.
-
A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology
S. Weichenthal et al.
-
Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India
M. Krishan et al.
-
iDetect for vulnerability detection in internet of things operating systems using machine learning
A. Al-Boghdady et al.
-
A hybrid CNN-Transformer model for ozone concentration prediction
Y. Chen et al.
-
Transforming air pollution management in India with AI and machine learning technologies
K. Rautela & M. Goyal
-
MapLUR
M. Steininger et al.
-
Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks
B. Peralta et al.
-
A Complete Air Pollution Monitoring and Prediction Framework
J. Kalajdjieski et al.
-
Image-Based Outdoor PM2.5 Concentration Estimation Using the BENet-VGG16 Model and Feature Engineering
H. Hsu & C. Wen
-
Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters
M. Kubara
-
Deep learning PM2.5 concentrations with bidirectional LSTM RNN
W. Tong et al.
-
BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer
A. Verma et al.
-
Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method
Z. Ye et al.
-
Forecasting air quality time series using deep learning
B. Freeman et al.
-
Deep learning for ocean temperature forecasting: a survey
X. Zhao et al.
-
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.
-
A multi-task stations cooperative air quality prediction system for sustainable development
B. Li & P. Wang
-
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.
-
Multimodal Imputation-Based Multimodal Autoencoder Framework for AQI Classification and Prediction of Indian Cities
R. Srinivasa Rao et al.
-
A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting
M. Zulqarnain et al.
-
Analysis of the Main Anthropogenic Sources’ Contribution to Pollutant Emissions in the Lazio Region, Italy
G. Battista et al.
-
Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks
J. Kalajdjieski et al.
-
Machine learning for observation bias correction with application to dust storm data assimilation
J. Jin et al.
-
Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework
M. Ahmed Hamza et al.
-
Deep learning methods for atmospheric PM2.5 prediction: A comparative study of transformer and CNN-LSTM-attention
B. Cui et al.
-
Temporally boosting neural network for improving dynamic prediction of PM2.5 concentration with changing and unbalanced distribution
H. Shi et al.
-
A Hybrid Autoformer Network for Air Pollution Forecasting Based on External Factor Optimization
K. Pan et al.
-
A time series forecasting based multi-criteria methodology for air quality prediction
R. Espinosa et al.
-
A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction
B. Zhang et al.
-
Überwachung der Sedierung in der Endoskopie mit künstlicher Intelligenz
J. Garbe et al.
-
A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance
A. Masood & K. Ahmad
-
Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM
J. Ma et al.
-
DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting
S. Fang et al.
-
A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network
G. Huang et al.
-
Exploring deep learning for air pollutant emission estimation
L. Huang et al.
-
Multi-Site and Multi-Pollutant Air Quality Data Modeling
M. Hu et al.
-
A review of Earth Artificial Intelligence
Z. Sun et al.
-
Modeling PM2.5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm
J. Gregório et al.
-
A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST
A. Verma et al.
-
Long-Term Air Quality Evaluation System Prediction In China Based On Multinomial Logistic Regression Method
Y. He et al.
-
An air quality prediction model based on improved Vanilla LSTM with multichannel input and multiroute output
W. Fang et al.
-
Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter
S. Lee & J. Shin
-
Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors
T. Do et al.
-
Air quality prediction using CT-LSTM
J. Wang et al.
-
Knowledge discovery from remote sensing images: A review
L. Wang et al.
-
PM2.5 forecasting under distribution shift: A graph learning approach
Y. Liu et al.
-
High return and low risk: Shaping composite financial investment decision in the new energy stock market
Q. Zhu et al.
-
A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction
S. Tsokov et al.
-
Traffic NO<sub>x</sub> Pollution Prediction and Health Cost Estimation Using Machine Learning: A Case Study of Toronto, Canada
H. Shamsi et al.
-
Resilience to Air Pollution: A Novel Approach for Detecting and Predicting Aerosol Atmospheric Rivers within Earth System Boundaries
K. Rautela et al.
-
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid
D. Iskandaryan et al.
-
Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review
F. Bahadur et al.
-
Regional air quality forecasting using spatiotemporal deep learning
S. Abirami & P. Chitra
-
Hybrid short-term traffic flow prediction based on the effect of non-linear sequence noise
G. Cheng & Y. Liu
-
Forecasting upper atmospheric scalars advection using deep learning: an $$O_3$$ experiment
L. Steffenel et al.
-
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.
-
Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito
P. Chau et al.
-
A Hybrid Deep Learning Model for Multi-step Ahead Prediction of PM2.5 Concentration Across India
P. Goswami et al.
-
Hyperparameter-Optimization-Inspired Long Short-Term Memory Network for Air Quality Grade Prediction
D. Wen et al.
-
A forecasting framework on fusion of spatiotemporal features for multi-station PM2.5
J. Wang et al.
-
A Methodological Comparison on Spatiotemporal Prediction of Criteria Air Pollutants
P. Singh et al.
-
Regional PM2.5 prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors
Y. Chen et al.
Latest update: 30 Apr 2026