Cutting-Edge Climate Analysis: Combining MLP-GRU and Remote Sensing Technologies
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41976-024-00128-w.pdf
Reference20 articles.
1. Fahad S et al (2023) “Implementing a novel deep learning technique for rainfall forecasting via climatic variables: an approach via hierarchical clustering analysis”, STE, Volume 854. ISSN 158760:0048–9697. https://doi.org/10.1016/j.scitotenv.2022.158760
2. Mostafa RR et al (2023) Modeling potential evapotranspiration by improved machine learning methods using limited climatic data. Water 15(3):486. https://doi.org/10.3390/w15030486
3. Sirmacek B et al (2022) “Remote sensing and AI for building climate adaptation applications”, RE, Volume 15. ISSN 100524:2590–1230. https://doi.org/10.1016/j.rineng.2022.100524
4. Lenton Timothy M et al (2024) Remotely sensing potential climate change tipping points across scales. Nat Commun 15:343. https://doi.org/10.1038/s41467-023-44609-w
5. Prabuddha MH Dewage et al (2024) "Providing fine temporal and spatial resolution analyses of airborne particulate matter utilizing complimentary in situ IoT sensor network and remote sensing approaches". RS 16(13):2454. https://doi.org/10.3390/rs16132454
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