ISSN 1000-3665 CN 11-2202/P
    秦同春,俞同云,王勇,等. AI技术在地面沉降研究中的应用与展望[J]. 水文地质工程地质,2024,51(0): 1-9. DOI: 10.16030/j.cnki.issn.1000-3665.202404049
    引用本文: 秦同春,俞同云,王勇,等. AI技术在地面沉降研究中的应用与展望[J]. 水文地质工程地质,2024,51(0): 1-9. DOI: 10.16030/j.cnki.issn.1000-3665.202404049
    QIN Tongchun, YU Tongyun, WANG Yong, et al. Application prospect of AI technology in the land subsidence analysis[J]. Hydrogeology & Engineering Geology, 2024, 51(0): 1-9. DOI: 10.16030/j.cnki.issn.1000-3665.202404049
    Citation: QIN Tongchun, YU Tongyun, WANG Yong, et al. Application prospect of AI technology in the land subsidence analysis[J]. Hydrogeology & Engineering Geology, 2024, 51(0): 1-9. DOI: 10.16030/j.cnki.issn.1000-3665.202404049

    AI技术在地面沉降研究中的应用与展望

    Application prospect of AI technology in the land subsidence analysis

    • 摘要: 地面沉降作为全球性的环境地质问题,对城市安全和可持续发展构成了严峻挑战。随着监测技术进步,传统的研究方法在大数据处理和监测预警面临速度和准确性的挑战,迫切需要新的技术手段以提高效率。人工智能(Artificial Intelligence ,AI)技术作为新质生产力的代表,在数据预处理、模型搭建、趋势预测等方面有着传统处理方法无法比拟的优势。文章对AI技术在地面沉降研究中的应用与展望进行综述研究,旨在将AI技术进一步引入地面沉降研究。文章首先对AI技术进行背景介绍,分析了其在地面沉降研究中的应用现状。随后,文章从监测预警系统以及宏观和微观机理研究的角度,对AI技术在地面沉降研究中的应用前景进行论述,分析了AI技术在提高研究效率和准确性方面的潜力。最后针对AI技术在地面沉降研究应用中的局限性,文章提出了工作建议。文章认为地面沉降研究需要AI技术进一步助力。将AI技术更深入地引入地面沉降研究,不仅有助于提高地面沉降研究的数据处理能力,还有可能为地面沉降的早期预警和有效防控提供新思路,有助于推动地面沉降研究领域的进步和发展。

       

      Abstract: As a global geo-environmental issue, land subsidence poses a severe challenge to urban safety and sustainable development. Traditional methods for land subsidence analysis face challenges in terms of speed and accuracy in big data processing and monitoring; it thus requires new approaches. Artificial Intelligence (AI) technology, as a representative of new quality productive forces, offers advantages in data preprocessing, model construction, and trend forecasting. This paper aims to further integrate AI into the study of land subsidence through a literature review. Initially, the paper provided a background introduction to AI and analyzed its current application in the field of land subsidence. The application of AI in land subsidence from the perspectives of monitoring and early warning systems, as well as macroscopic and microscopic mechanism studies was then prospected, highlighting the potential of AI in improving efficiency and accuracy. Finally, this review analyzed the limitations of AI and proposed suggestions in the future. It states that the study on land subsidence requires further assistance from AI technology. A deeper integration of AI technology into land subsidence analysis will not only improve data processing capabilities but also provide new insights into the early warning and effective control of land subsidence, contributing to the advancement and development of the field of land subsidence.

       

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