混合深度学习-数值建模框架,用于长期预测地下水排放和放射性核素运输
Minkyeong Seong1, Hyo Gyeom Kim2, Byeongchan Yun1
1School of Civil, Environmental, and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea.
Journal of hazardous materials
|February 6, 2026
概括
本研究引入了一种混合模型,将数值模拟和深度学习结合起来,用于在地质处置系统中准确的长期地下水流和放射性核素运输预测. 这种新的方法显著提高了效率和可靠性.
科学领域:
- 地质科学 地质科学
- 水文地质学 水文地质学
- 人工智能的人工智能
背景情况:
- 准确的地下水流和放射性核素运输的长期预测对于深层地质处置系统的安全评估至关重要.
- 现有的数值模型可能是计算密集型,限制了它们在综合性绩效评估中的应用.
研究的目的:
- 开发和评估混合建模框架,将数值模拟与深度学习相结合,以提高地下水排放和放射性核素运输的预测准确性和计算效率.
- 根据已建立的数值模型评估拟议框架的性能.
主要方法:
- 开发了一个混合框架,使用基于自适应过程的总系统性能评估框架 (APro-BIO) 的输出作为输入特征.
- 使用HydroGeoSphere (HGS) 模拟的每月地下水排放和放射性核素运输数据作为目标变量,训练了一个图形卷积长短期记忆 (GC-LSTM) 模型.
- 使用可解释的AI技术来识别关键的有影响力的参数.
主要成果:
- GC-LSTM模型实现了高预测性能,Kling-Gupta效率值在地下水排放的范围为0.67-0.85,放射性核素运输的范围为0.60-0.81.
- 混合模型与APro-BIO模型相比,减少了高达99%的差异,显示出精度和效率的显著提高.
- 可解释的人工智能分析强调了范·格努赫特的β参数作为预测最有影响力的因素.
结论:
- 拟议的混合建模框架为长期地下水排放和放射性核素运输预测提供了一种高效可靠的替代方案.
- 这种方法对于具有计算限制的应用特别有价值,例如深层地质存储库的安全评估.
- 深度学习与数值模型的整合增强了预测能力,同时保持了可解释性.
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