A comparative analysis of deep learning models for soil temperature prediction in cold climates

Accurate soil temperature prediction in cold climates is crucial for optimizing agricultural practices, hydrological processes, water resource management, minimizing frost damage, and mitigating flood risks. The capacity of deep learning methods to capture intricate patterns and relationships in cli...

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Veröffentlicht in:	Theoretical and applied climatology 2024-04, Vol.155 (4), p.2571-2587
Hauptverfasser:	Imanian, Hanifeh, Mohammadian, Abdolmajid, Farhangmehr, Vahid, Payeur, Pierre, Goodarzi, Danial, Hiedra Cobo, Juan, Shirkhani, Hamidreza
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Agricultural practices Aquatic Pollution Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate change Climate prediction Climatic data Climatology Cold climates Cold weather Comparative analysis Deep learning Earth and Environmental Science Earth Sciences Environmental impact Environmental risk Flood damage Flood management Flood risk Freezing Freezing point Frost damage Hydrologic processes Learning algorithms Long short-term memory Machine learning Melting points Multilayer perceptrons Multilayers Neural networks Polar climates Representations Resource management Sequencing Soil Soil layers Soil temperature Soils Temperature Temperature perception Time series Waste Water Technology Water Management Water Pollution Control Water resources Water resources management
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