WaterGPT: Training a Large Language Model to Become a Hydrology Expert DOI Open Access
Yi Ren, Tianyi Zhang,

Xurong Dong

et al.

Water, Journal Year: 2024, Volume and Issue: 16(21), P. 3075 - 3075

Published: Oct. 27, 2024

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied three main areas: (1) processing and analyzing data such as images text water resources, (2) supporting intelligent decision-making hydrological tasks, (3) enabling interdisciplinary information integration knowledge-based Q&A. The has achieved promising results. One core aspect of involves the meticulous segmentation training supervised fine-tuning phase, sourced from real-world annotated with high quality using both manual methods GPT-series annotations. These are carefully categorized into four types: knowledge-based, task-oriented, negative samples, multi-turn dialogues. Additionally, another key component development multi-agent framework called Water_Agent, which enables to intelligently invoke various tools solve field resources. handles data, including images, allowing deep understanding analysis environments. Based on this framework, over 90% success rate object detection waterbody extraction. For extraction task, Dice mIoU metrics, WaterGPT’s performance high-resolution 2013 2022 remained stable, accuracy exceeding 90%. Moreover, we have constructed high-quality resources evaluation dataset, EvalWater, covers 21 categories approximately 10,000 questions. Using highest date reaching 83.09%, about 17.83 points higher than GPT-4.

Language: Английский

Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs DOI

Yuqian Hu,

Heng Li, Chunxiao Zhang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 653, P. 132766 - 132766

Published: Jan. 29, 2025

Language: Английский

Citations

0
Emerging Fields in Hydrology DOI
Vijay P. Singh, Ximing Cai,

Solomon Vimal

et al.

Journal of Hydrologic Engineering, Journal Year: 2025, Volume and Issue: 30(2)

Published: Feb. 7, 2025

Citations

0
Progress and gaps in ecological risk under climate change research: A bibliometric and literature review approach DOI
Yi Wang, Yihe Lü,

Da Lü

et al.

Transactions in Earth Environment and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

In the past 20 years, ecological risks arising from climate change have attracted increasing attention. Understanding its research progress and evolution of hot topics is paramount. However, efficient, in-depth, robust analysis massive complex unstructured literature difficult. This study employs a novel approach integrating data mining, bibliometrics, systematic review to analyze 9122 interdisciplinary publications 2000 2023. Our findings reveal consistent annual increase in publications, with marked acceleration post-2015. The United States China emerged as leading contributors this field. Over time, theme has traversed three pivotal hotspots over 100 words. We summarized early late stages into nine aspects: (1) species population responses; (2) ecosystem impacts; (3) social-ecological system risks; (4) land use/cover interactions; (5) processes; (6) services; (7) sustainable development goals; (8) conservation, management, adaptation; (9) risk assessment major models. Additionally, we international policies efforts combat risks, gaps, potential directions for future progress: establish unified comparable regional framework; strengthen on processes, multiple sources pressure, composite enhance space-time flow high-precision basic datasets; improve communication cooperation among stakeholders. provides comprehensive change’s which may inspire researchers interested

Language: Английский

Citations

0
On the value of a history of hydrology and the establishment of a History of Hydrology Working Group DOI Creative Commons
Keith Beven, S. A. Archfield, Okke Batelaan

et al.

Hydrological Sciences Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: Feb. 24, 2025

Language: Английский

Citations

0
WaterGPT: Training a Large Language Model to Become a Hydrology Expert DOI Open Access
Yi Ren, Tianyi Zhang,

Xurong Dong

et al.

Water, Journal Year: 2024, Volume and Issue: 16(21), P. 3075 - 3075

Published: Oct. 27, 2024

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied three main areas: (1) processing and analyzing data such as images text water resources, (2) supporting intelligent decision-making hydrological tasks, (3) enabling interdisciplinary information integration knowledge-based Q&A. The has achieved promising results. One core aspect of involves the meticulous segmentation training supervised fine-tuning phase, sourced from real-world annotated with high quality using both manual methods GPT-series annotations. These are carefully categorized into four types: knowledge-based, task-oriented, negative samples, multi-turn dialogues. Additionally, another key component development multi-agent framework called Water_Agent, which enables to intelligently invoke various tools solve field resources. handles data, including images, allowing deep understanding analysis environments. Based on this framework, over 90% success rate object detection waterbody extraction. For extraction task, Dice mIoU metrics, WaterGPT’s performance high-resolution 2013 2022 remained stable, accuracy exceeding 90%. Moreover, we have constructed high-quality resources evaluation dataset, EvalWater, covers 21 categories approximately 10,000 questions. Using highest date reaching 83.09%, about 17.83 points higher than GPT-4.

Language: Английский

Citations

0