基于二次分解和双向门反复单位的溶解氧度的混合预测模型
Jiange Jiao1,2, Qianqian Ma1, Fanglin Liu1
1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, China.
Environmental geochemistry and health
|March 14, 2024
概括
准确的溶氧预测对于水质管理至关重要. 本研究引入了一种使用二次分解和双向门循环单元 (BiGRU) 的新型混合模型,以有效预测河流中溶解氧水平.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 时间序列预测时间序列预测
背景情况:
- 溶解氧 (DO) 是河水质量和污染水平的关键指标.
- 准确的DO监测和预测对于保持生态平衡和预防环境问题至关重要.
- 现有的模型难以捕捉DO序列的动态和非线性特征,影响预测准确性.
研究的目的:
- 为溶解氧度开发一种混合预测模型,解决单个模型的局限性.
- 通过处理高挥发性和非线性数据来提高溶解氧预测的准确性.
- 为水环境管理和生态保护提供可靠的工具.
主要方法:
- 一个新的混合模型,结合了二次分解和双向门循环单元 (BiGRU) 神经网络.
- 使用完整集体实证模式分解与自适应噪声 (CEEMDAN) 进行DO序列的初步分解.
- 高内在模式函数 (IMFs) 的二次分解使用由北方Goshawk优化 (GNO-VMD) 优化的变量模式分解.
- 使用BiGRU预测每个分解的成分,并为最终的DO度预测进行重建.
主要成果:
- 拟议的模型在预测新安水库中溶解的氧气水平方面表现出卓越的性能.
- 取得了优异的准确度指标:RMSE为0.1164,MAE为0.0894,MAPE为1.0403%,R2为0.9939. 这些指标均为1.0403%.
- 超过了各种基准模型,包括BP,LSTM,GRU,BiGRU和基于单个分解的混合模型.
结论:
- 混合模型有效地处理溶解氧数据固有的高波动性和非线性.
- 二次分解方法通过稳定高频模式,显著提高了预测准确性.
- 这种先进的预测模型为有效的水资源管理和生态保护工作提供了宝贵的见解.
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