相关实验视频
Updated: Jul 20, 2025

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
Published on: November 13, 2017
根据VMD-HHO-KELM模型,根据洪水季节的每日排水预测
Xianqi Zhang1, Fang Liu2, Qiuwen Yin2
1Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China; Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou 450046, China; Technology Research Center of Water Conservancy and Marine Traffic Engineering, Zhengzhou, Henan Province 450046, China
一个新的VMD-HHO-KELM模型提高了黄河每日排水预测的准确性. 这种方法通过解决非线性河流流特征来加强洪水控制和水库管理.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 每天在黄河下游预测流量,对于有效的洪水控制和水库管理至关重要.
- 流水数据显示由于气象,气候变化和人类活动的影响而表现出非静止和非线性模式.
研究的目的:
- 为黄河下游开发一个准确的每日流量预测模型.
- 为了减轻流出时间序列数据中固有的非线性和非平滑性.
主要方法:
- 提出了一个新的组合模型:与哈里斯霍克优化 (HHO) 优化的内核极端学习机器 (KELM) 集成的变量模态分解 (VMD).
- 应用VMD-HHO-KELM模型来预测高和丽水文站的每日排水量.
主要成果:
- 在两个站点上,VMD-HHO-KELM模型表现出卓越的预测准确性.
- 在高站,R2达到0.95,MAE为13.3,RMSE为33.83.
- 在丽站,R2达到0.96,MAE为8.03和RMSE为38.45.
结论:
- 该VMD-HHO-KELM模型显著提高了每日排水预测的准确性.
- 这种先进的建模方法为黄河流域的水文预测和水资源管理提供了更好的能力.
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