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基于合VMD-SSA-BiLSTM模型的每月流失预测
Xianqi Zhang1,2,3, Xin Wang4, Haiyang Li1
1Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Scientific reports
|August 12, 2023
概括
一个新的VMD-SSA-BiLSTM模型准确地预测了每月的黄河流量. 这种混合方法通过提高对现有方法的预测准确性来改善水资源管理和防洪.
科学领域:
- 水文和水资源工程 水文和水资源工程
- 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:
- 准确的每月黄河下游排水预测对于水资源管理,分配和洪水控制至关重要.
- 现有的预测模型可能无法完全捕捉到河流流失过程的复杂动态.
研究的目的:
- 开发和评估一种新的混合模型,变量模态分解-搜索算法-双向长期和短期记忆神经网络 (VMD-SSA-BiLSTM),用于每月的流失预测.
- 评估VMD-SSA-BiLSTM模型与传统BiLSTM和VMD-BiLSTM模型的性能.
主要方法:
- 变量模态分解 (VMD) 用于信号分解和流量数据的预处理.
- 使用Sparrow搜索算法 (SSA) 来优化双向长期和短期记忆神经网络 (BiLSTM) 的参数.
- 结合的VMD-SSA-BiLSTM模型被应用来预测GaoCun水文站的每月排水量.
主要成果:
- 与BiLSTM和VMD-BiLSTM模型相比,VMD-SSA-BiLSTM模型显示出更高的预测准确度.
- 在根-平均-平方偏差 (RMSD),平均绝对百分比误差 (MAPE),平均绝对误差 (MAE),确定系数 (R2) 和纳什-萨特克利夫效率 (NSE) 中观察到显著改善.
- 具体来说,RMSD下降了242.5124和39.9835,MAPE下降了35.5937%和6.3856%,MAE下降了136.7288和25.7274,R2上升了0.53059和0.14739,NSE上升了0.4994和0.1122,分别.
结论:
- VMD-SSA-BiLSTM模型有效地提高了每月排水系列的顺性,并提高了点预测的准确性.
- 拟议的混合模型为在黄河下游流域可靠的每月流量预测提供了一个有希望的工具.
- 这种方法有助于更好地管理水资源和减轻洪水风险的战略.
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