Peer-reviewedCC BY 4.0Scientific Reports
G2RL: grid-aware gated recurrent learning for spatiotemporal urban air quality forecasting
Related
- Deep learning framework for weather aware spatiotemporal traffic speed forecasting on mountainous tourist roads
- Event-Aware Spatiotemporal Precipitation Forecasting with Geographic Context and Physics-Guided Regularization
- CAT-GAN-UKF: uncertainty-aware spatiotemporal reconstruction for resilient urban air-quality monitoring under partial observability
- Cost-aware evaluation of time-series foundation models for urban air-quality and temperature forecasting
- Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification
BibTeX
@article{chatterjee2026g2rl,
title = {{G2RL: grid-aware gated recurrent learning for spatiotemporal urban air quality forecasting}},
author = {Kalyan Chatterjee and Bhoomeshwar Bala and Mudassir Khan and Mada Prasad and Raja Shekar Kadurka and Meteb Altaf and Fatimah Alhayan and Hanan M. Alasmari and Prudhvi Naayini and Katla Aruna Jyothi},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-77469-7},
url = {https://www.nature.com/articles/s41598-026-77469-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - G2RL: grid-aware gated recurrent learning for spatiotemporal urban air quality forecasting
AU - Kalyan Chatterjee
AU - Bhoomeshwar Bala
AU - Mudassir Khan
AU - Mada Prasad
AU - Raja Shekar Kadurka
AU - Meteb Altaf
AU - Fatimah Alhayan
AU - Hanan M. Alasmari
AU - Prudhvi Naayini
AU - Katla Aruna Jyothi
PY - 2026
DA - 2026/11/07
JO - Scientific Reports
DO - 10.1038/s41598-026-77469-7
UR - https://www.nature.com/articles/s41598-026-77469-7
SN - 2045-2322
ER -