在河口水域使用自适应性参数化物理信息的神经网络预测悬浮的大小
Ya Wu1, Leiping Ye1, Jie Ren1
1School of Marine Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, PR China.
Water research
|June 28, 2025
概括
一个新的自适应性参数化物理信息神经网络 (SAP-PINNs) 模型提高了河口水域的大小预测准确度. 这种先进的方法通过动态优化参数来改善沉积物运输建模和生态评估.
科学领域:
- 环境科学 环境科学
- 流体动力学 流体动力学
- 机器学习 机器学习
背景情况:
- 悬浮的大小动态对于沉积物运输和河口生态至关重要.
- 由于固定的参数,传统的花化模型在复杂的水力动力学条件下难以准确.
研究的目的:
- 开发一种新的自适应性参数化物理信息神经网络 (SAP-PINNs) 模型,用于准确的群体大小预测.
- 提高花动态模型的适应性和物理一致性.
主要方法:
- 实现了一个SAP-PINNs模型,可以动态优化聚合,断裂和侵蚀参数.
- 集成的数据驱动机器学习,具有物理约束,用于改进建模.
- 使用实验室实验在不同的剪压和现场数据下验证了模型.
主要成果:
- SAP-PINNs模型在可变水力动力学模式中表现出稳定性,准确预测流量大小.
- 与传统模型相比,现场验证显示精度增加了88.31%,R2为0.99,MAE为0.78.
- SHAP分析确定了剪切应力和盐度是关键驱动因素,悬浮沉积物度具有最佳范围效应.
结论:
- SAP-PINNs有效地结合了物理和机器学习,在流动动力学中实现了卓越的准确性,可解释性和可概括性.
- 该模型在复杂的水力动力学系统中具有很大的应用潜力,有助于沉积物运输和水质管理.
- 这种方法提高了对河口和沿海环境的预测能力.
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