一个多尺度的CNN-GRU融合模型与静止波波变换为14天前的水位预测大水位预测
Kai Wen Ng1, Yuk Feng Huang2, Chai Hoon Koo1
1Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sg. Long, Bandar Sg. Long, Cheras, 43000, Kajang, Selangor, Malaysia.
Scientific reports
|November 21, 2025
概括
波形分解和多尺度CNN显著改善了14天的水位预测. 这些先进技术增强了融合模型,为水资源管理提供了更准确的预测.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境科学中的人工智能
- 时间序列预测时间序列预测
背景情况:
- 准确的水位预测对于水运营和水资源管理至关重要.
- 传统的预测模型经常与水文数据的复杂,多变量性质作斗争.
- 加强像CNN-GRU这样的深度学习模型对于提高预测准确性至关重要.
研究的目的:
- 评估静止波形变换 (SWT) 数据分解在改善CNN-GRU聚变模型14天水位预测中的有效性.
- 研究将多尺度卷积神经网络 (CNN) 模块纳入 CNN-GRU 融合架构的影响.
- 确定数据分解和模型架构的最佳组合,以提高水文预测.
主要方法:
- 使用的多变量时间序列数据包括每日水位,降雨量,蒸发,月度水需求,ONI,SOI和DMI.
- 应用静止波形变换 (SWT) 用于关键水文变量的数据分解.
- 开发并比较CNN-GRU早期,缓慢和晚期融合模型,结合标准CNN和多尺度CNN模块.
主要成果:
- SWT数据分解显著提高了所有测试的CNN-GRU融合模型的性能.
- 每日水位时间序列的分解产生了最实质性的性能提升.
- 多尺度CNN模块进一步提升了模型性能,特别是在缓慢和晚期融合架构中,通过在各种尺度上捕捉特征.
- 最好的性能是通过CNN-GRU缓慢融合模型与多尺度CNN模块实现的,在SWT分解数据上训练,产生NRMSE的0.4144,NSE的0.7919和MAPE的0.318.
结论:
- 静止波形变换 (SWT) 数据分解是一个非常有效的预处理步骤,用于增强基于深度学习的水位预测模型.
- 多尺度CNN模块的集成为水文时间序列预测的特征提取提供了显著的优势.
- 优化的CNN-GRU缓慢聚变模型,利用SWT分解和多尺度CNN,为14天前的水位预测提供了强大而准确的解决方案.
相关概念视频
01:12Modeling and Similitude
588
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
588
01:30Typical Model Studies
607
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
607
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
647
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
647
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
270
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
270