The importance of artificial intelligence-based methods in precipitation modeling studies: a bibliometric analysis

Modeling climate parameters is essential for understanding climate variability, tracking changes over time, adapting to climate change, and assessing its impacts. Precipitation is especially important in climate science because it significantly influences ecosystems, agriculture, extreme weather eve...

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Veröffentlicht in:	Theoretical and applied climatology Jg. 156; H. 11; S. 602
Hauptverfasser:	Aydin, Olgu, Kilar, Hatice
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 01.11.2025 Springer Nature B.V
Schlagworte:	Accuracy Agricultural ecosystems Agricultural production Aquatic Pollution Artificial intelligence Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Bibliometrics Climate adaptation Climate change Climate change adaptation Climate models Climate science Climate variability Climatic analysis Climatology Datasets Decision making Deep learning Earth and Environmental Science Earth Sciences Extreme weather Hydrologic cycle Hydrological cycle Hydrology Learning algorithms Machine learning Modelling Neural networks Precipitation Rain Rainfall runoff Rainfall-runoff relationships Waste Water Technology Water Management Water Pollution Control Water resources management
ISSN:	0177-798X, 1434-4483
Online-Zugang:	Volltext
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