重力坝渗流预测的人工智能方法综述、挑战与出路

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Priyanka Ashok Garsole, Shantini A. Bokil, Vijendra Kumar, Arunabh Pandey, Niraj S. Topare
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引用次数: 0

摘要

渗漏是水渗入重力坝地基的现象,随着时间的推移,会造成侵蚀并削弱大坝的结构。如果管理不当,最终可能导致大坝的灾难性故障,对公共安全和环境构成重大威胁。因此,精确的渗流预测对保证重力坝的安全稳定至关重要。本文综述了人工智能(AI)技术在重力坝渗流预测中的应用,以及面临的挑战和可能的解决方案。本文确定并提出了与使用人工智能进行渗流预测相关的挑战的潜在解决方案,例如数据质量和模型可解释性。该论文还涵盖了未来的研究路径,例如创建先进的机器学习算法以及改进数据收集和处理。总体而言,本文综述了目前使用人工智能预测重力坝渗流的技术现状,并提出了提高此类模型准确性和可靠性的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review of artificial intelligence methods for predicting gravity dam seepage, challenges and way-out
Seepage is the phenomenon of water infiltrating through a gravity dam's foundation, causing erosion and weakening the dam's construction over time. If not properly managed, this can eventually lead to the dam's catastrophic failure, posing a significant danger to public safety and the environment. As a result, precise seepage prediction in gravity dams is essential for ensuring their safety and stability. This review paper looks at the use of artificial intelligence (AI) techniques for predicting seepage in gravity dams, as well as the challenges and possible solutions. The paper identifies and suggests potential solutions to the challenges connected with using AI for seepage prediction, such as data quality and model interpretability. The paper also covers future research paths, such as the creation of advanced machine learning algorithms and the improvement of data collection and processing. Overall, this review gives insight on the current state of the art in using AI to predict gravity dam seepage and recommends methods to improve the accuracy and reliability of such models.
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来源期刊
CiteScore
4.10
自引率
21.10%
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