基于VDD的不同分解方法进行降雨预测的比较研究
Xianqi Zhang1,2,3, Qiuwen Yin4, Fang Liu1
1Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Scientific reports
|November 18, 2023
概括
准确的降雨预测使用了一种新的VMD分解方法,与优化的神经网络相结合. 这种方法显著提高了预测准确度,有助于水资源管理和预防灾害.
科学领域:
- 水文和水资源水文与水资源
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 有效的降雨预报对于水资源管理和减灾至关重要.
- 传统的预测方法往往在准确性和适应性方面面临挑战.
- 变化模式分解 (VMD) 为复杂时间序列中的信号分析提供了潜力.
研究的目的:
- 评估不同变化模式分解 (VMD) 策略的降雨预测效果.
- 开发和测试一个改进的预测模型,将VMD与先进的优化和神经网络技术集成在一起.
- 通过使用现实世界的数据,比较拟议模型与传统方法的性能.
主要方法:
- 变量模式分解 (VMD) 采用完全和阶段性分解方法.
- 修改的非洲优化算法 (MAVOA) 增强了帐混乱映射.
- 微分神经计算机 (DNC) 集成循环神经网络和计算处理.
- 使用来自华河流域四个地点的数据进行预测模型的比较分析.
主要成果:
- 单一模型完全阶段分解 (SMFSD) VMD方法与MAVOA-DNC相结合,表现出卓越的性能.
- 在SMFSD-MAVOA-DNC模型中,平均根平均平方误差 (RMSE) 为9.02,平均绝对误差 (MAE) 为7.13.
- 该模型达到0.94的Nash-Sutcliffe平均效率 (NSE),优于传统的VMD全分解和其他合模型.
结论:
- VMD的逐步分解,当与MAVOA-DNC模型集成时,提供了一个非常有效的降雨预测解决方案.
- 与传统方法相比,拟议的SMFSD-MAVOA-DNC模型显著提高了预测准确性.
- 这种先进的预测方法为水资源管理和防灾准备提供了实质性的改进.
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