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Exploring the application of large AI models in flash flood risk identification and early warning
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Jiyang TIAN1, 2, Yuefen ZHANG1, 2, Denghua YAN1, 2, Shenghao XU2, 3, Jiacheng DUAN2, 3, Jianzhu LI3
Journal of Hydraulic Engineering | 2026, 57(5) : 675 - 690
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Journal of Hydraulic Engineering | 2026, 57(5): 675-690
Exploring the application of large AI models in flash flood risk identification and early warning
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Jiyang TIAN1, 2, Yuefen ZHANG1, 2, Denghua YAN1, 2, Shenghao XU2, 3, Jiacheng DUAN2, 3, Jianzhu LI3
Affiliations
  • 1.State Key Laboratory of Water Cycle and Water Security,China Institute of Water Resources and Hydropower Research,Beijing 100038,China
  • 2.Research Center on Flood and Drought Disaster Reduction,MWR,Beijing 100038,China
  • 3.State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation,Tianjin University,Tianjin 300350,China
Published: 2026-05-20 doi: 10.3724/j.slxb.20250412
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Flash flood disasters are the main cause of fatalities among flood-related hazards in China. Risk identification and early warning represent crucial technologies for the proactive defense against flash floods. This paper systematically reviews conventional techniques for flash flood risk identification and early warning both within China and abroad, providing a detailed analysis of the advantages, limitations and bottlenecks of various methods. By tracing the development of artificial intelligence (AI) and its role in transforming research paradigms in hydrological science, the study highlights the significant potential of AI in flash flood disaster prevention. It identifies six major challenges confronting large AI models in the context of flash flood risk identification and early warning: data acquisition and quality, generalization and interpretability, balancing complexity with emergent capabilities, computational efficiency and parallel acceleration, and intelligent decision-making. Targeted solutions and future trends are discussed in response to these challenges. The paper proposes that future research should focus on big data governance, and the integration technology of AI and physical models. Additionally, efforts should focus on continuously improving heterogeneous hybrid parallel computing framework and training strategies, advancing automated optimization of large model parameters and intelligent prediction, and enhancing learning capacity, generalization ability and risk prediction ability. The aim of these efforts is to provide both theoretical insights and practical guidance for the application of large AI models in flash flood risk identification and early warning.

flash flood disaster  /  large AI models  /  integration with physical models  /  intelligent prediction  /  risk identification and early warning
Jiyang TIAN, Yuefen ZHANG, Denghua YAN, Shenghao XU, Jiacheng DUAN, Jianzhu LI. Exploring the application of large AI models in flash flood risk identification and early warning[J]. Journal of Hydraulic Engineering, 2026 , 57 (5) : 675 -690 . DOI: 10.3724/j.slxb.20250412
Year 2026 volume 57 Issue 5
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Article Info
doi: 10.3724/j.slxb.20250412
  • Receive Date:2025-07-28
  • Online Date:2026-06-25
  • Published:2026-05-20
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  • Received:2025-07-28
Affiliations
    1.State Key Laboratory of Water Cycle and Water Security,China Institute of Water Resources and Hydropower Research,Beijing 100038,China
    2.Research Center on Flood and Drought Disaster Reduction,MWR,Beijing 100038,China
    3.State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation,Tianjin University,Tianjin 300350,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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