可解释的深度学习用于动态降雨-流水预测:集成自适应信号分解和时空特征提取
Xuan Xie1, Guohe Huang2, Shuguang Wang3
1School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China.
Journal of environmental management
|December 30, 2025
概括
这项研究引入了一种先进的降雨-流水预测模型,使用自适应信号分解和时空注意力. 该模型显著提高了预测准确性,特别是在短期预测方面,优于传统方法.
科学领域:
- 水文与水资源工程 水文与水资源工程
- 环境科学 环境科学
- 数据科学和机器学习
背景情况:
- 准确的排水预测对于有效的水资源管理至关重要.
- 流水数据通常由于复杂的空间和时间因素而表现出复杂的非线性和非静止特征.
- 现有的模型难以捕捉这些复杂性,限制了预测准确性.
研究的目的:
- 开发一个改进的降雨-流水预测模型,集成自适应信号分解和时空特征提取.
- 提高流量预测模型的准确性和可解释性.
- 为模拟复杂的非线性时空数据提供洞察力.
主要方法:
- 适应性粒子群集优化变化模式分解 (APSO-VMD) 用于多尺度信号分解和消噪.
- 时空注意力机制,以捕捉时间依赖和空间驱动关系.
- 用于动态降雨-流水关系建模的门式循环单位 (GRU).
主要成果:
- 拟议的模型在各种预测视野中显著优于传统和组合模型.
- 在短期预测中表现异常,NSE=0.9977和RMSE=1.1793.
- 数据分解模块显示了最强的主要效应 (高达62.1%),其与时空特征提取的相互作用有显著的贡献.
结论:
- 综合方法有效地解决了流水数据的非线性和非静止性.
- 该模型为降雨-流水预测提供了更高的准确性和可解释性.
- 这项研究为复杂的时空水文数据的建模和预测提供了一个强大的框架.
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