Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

Gaofeng Xu; Lingqian Wu

doi:10.1117/12.3011513

7 December 2023 Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning

Gaofeng Xu, Lingqian Wu

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129414R (2023) https://doi.org/10.1117/12.3011513
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

With the research on the application of big data and artificial intelligence technology to geological disaster risk monitoring and early warning, the relevant dimensions are disassembled more and more carefully, and the results are more accurate. Based on the hydrogeological conditions of the river basin, combined with major national, regional, and local water resource needs and natural resource management requirements, coordinate the groundwater level measurement and groundwater resource evaluation work in the Yangtze River Basin, integrate the water resource achievements of the Yangtze River Basin through data translation and summary, establish a conceptual water resource model for the Yangtze River Basin, conduct water resource carrying capacity evaluation, and propose water resources, water ecology, and Earth science solutions to water environment issues, supporting the work of poverty alleviation and water resource management centers.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gaofeng Xu and Lingqian Wu "Research on big data and artificial intelligence technology in geological disaster risk monitoring and early warning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129414R (7 December 2023); https://doi.org/10.1117/12.3011513

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