Transformer based models with hierarchical graph representations for enhanced climate forecasting

Accurate climate predictions are essential for agriculture, urban planning, and disaster management. Traditional forecasting methods often struggle with regional accuracy, computational demands, and scalability. This study proposes a Transformer-based deep learning model for daily temperature foreca...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 23464 - 22
Hauptverfasser:	Ramu, T. Bhargava, Kocherla, Raviteja, Sirisha, G. N. V. G., Chetana, V. Lakshmi, Sagar, P. Vidya, Balamurali, R., Boddu, Nanditha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 02.07.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/705 Accuracy Algorithms Case studies Climate change Climate prediction Climatic data Computer applications Decision making Deep learning Disaster management Emergency preparedness Feature optimization Feature selection Forecasting Graph representations Hierarchical graph modeling Humanities and Social Sciences Machine learning multidisciplinary Neural networks Permafrost Precipitation Rain Science Science (multidisciplinary) Storms Temperature Transformer-based forecasting Urban agriculture Urban planning Variables Weather forecasting Pacific Northwest Deep learning Transformer-based forecasting Feature optimization Hierarchical graph modeling Climate prediction
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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