混合式门式循环单元具有变化模式分解和一个错误补偿机制,用于多步前期的每月降雨预测
Deyun Wang1,2, Yifei Ren3, Yanchen Yang4
1School of Economics and Management, China University of Geosciences, Wuhan, 430074, China. wangdy@cug.edu.cn.
Environmental science and pollution research international
|December 1, 2023
概括
这项研究引入了一种新的GRU-VMD-ECM模型,用于高精度的每月降雨预测. 该模型显著提高了预测准确性,有助于灾害准备和政府决策.
科学领域:
- 水文和气候科学 水文和气候科学
- 环境监测中的人工智能
- 时间序列预测时间序列预测
背景情况:
- 准确的每月降雨预测对于减轻洪水和干旱的影响至关重要.
- 现有的预测模型往往在多步预测和准确性方面扎.
- 及时的警告使政府能够实施有效的灾害管理战略.
研究的目的:
- 开发和验证一个高度准确的前进的多步月度降雨预测模型.
- 通过使用一种新的错误补偿机制,提高降雨预测的精度.
- 为预警系统和政府决策提供可靠的数据.
主要方法:
- 开发了一个两相模型,集成了Gated Recurrent Unit (GRU),变化模式分解 (VMD) 和错误补偿机制 (ECM) (GRU-VMD-ECM).
- 该GRU模型提供了最初的预测,随后VMD将错误分解成八个子系列.
- 每个VMD子系列都用于训练个人GRU模型进行错误预测和补偿.
主要成果:
- GRU-VMD-ECM模型在预测准确度方面取得了显著的改进.
- 纳什-萨特克利夫效率 (NSE) 增加了281.16% (0.259981至0.990944) 对于单步前进预测.
- 根平均平方误差 (RMSE) 从2.257580降至0.249746,超过了其他模型,如RF和GRU-CNN.
结论:
- 拟议的GRU-VMD-ECM模型为每月降雨预测提供了卓越的准确性.
- 错误补偿机制在提高预测精度方面非常有效.
- 该模型在预警系统和气候适应规划中具有强大的实际应用潜力.
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