Over the past three decades, China's unprecedented economic growth and rapid urbanization have brought major challenges in water resource management, flood control, and ecological protection. This demand has driven a rapid expansion and evolution in hydrological research. However, the exponential growth of scientific literature makes it increasingly difficult for individual researchers to quantitatively grasp the field’s development and shifting trends, particularly when trying to extract specific geographic locations or thematic nuances from publication abstracts.
In a study published in Fundamental Research by a team of researchers led by Professor Chiyuan Miao from Beijing Normal University, a fine-tuned Large Language Model paired with geocoding tools to automatically “read” and parse complex basin information from 289,513 global publications was deployed.
"Traditional reviews inherently reflect qualitative assessments shaped by researchers' personal expertise and perspectives," explains Miao. "Leveraging advanced artificial intelligence techniques, such as LLM and topic modeling, we have achieved automated processing at an unprecedented scale, isolating 4,177 highly relevant studies specifically focusing on China's major basins."
The team's extensive data analysis highlights several crucial milestones in the development of Chinese hydrology
The team hopes their AI-empowered approach will not only trace the historical trajectory of Chinese hydrology but also guide future research priorities and sustainable water resource strategies worldwide.
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Contact the author: Chiyuan Miao, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China, miaocy@bnu.edu.cn
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Fundamental Research
Data/statistical analysis
Not applicable
Advances in hydrological research in China over the past two decades: Insights from advanced large language model and topic modeling
The authors declare that they have no conflicts of interest in this work.