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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Zusammenfassung: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 events, and the hydrological cycle. In this context, using Artificial Intelligence (AI), Artificial Neural Networks (ANN), Machine Learning (ML), and Deep Learning (DL) methods in precipitation modeling has become a key area of research. This study was conducted using the “Clarivate Analytics Web of Science (WoS)” database on September 17, 2024. A total of 112,721 articles that utilized AI methods in precipitation modeling from 1995 to 2023 were reviewed. These articles were ranked by citation count, leading to the selection of 238 papers for further analysis. The study focuses on three time periods: 1995–2004, 2005–2014, and 2015–2023. The 238 identified articles received a total of 42,351 citations, averaging 177.95 citations per article. The average citation count was highest in the first period (1995–2004) but declined in the 2015–2023 period. The journal with the most citations is “ Atmospheric Environment ,” and the most cited paper is by Gardner and Dorling ( 1998 ). The “ Journal of Hydrology ” has the highest H-index at 40. The most commonly used term in publications is “ machine learning ,” along with other important terms like “ precipitation ,” “ artificial neural networks , ” “ deep learning , ” “rainfall , ” and “ rainfall-runoff .” In conclusion, this study provides a bibliometric analysis of key topics related to precipitation modeling from 1995 to 2023, highlighting directions for future research.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-025-05837-w