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基于CEEMDAN-AdaBoost-BiLSTM-LSTM模型的甘河溶解氧的多步预测

Neha Pant¹, Durga Toshniwal², Bhola Ram Gurjar³

¹Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India.

Scientific reports

|May 16, 2024

概括

这项研究引入了一个新的CEEMDAN-AdaBoost-BiLSTM-LSTM模型,用于准确的溶化氧 (DO) 预测. 该方法通过量身定制的IMF分析来提高预测准确性,从而提高水资源管理.

科学领域:

环境科学环境科学
水资源管理水资源管理
数据科学数据科学数据科学

背景情况:

准确的溶解氧 (DO) 预测对于有效的水资源管理至关重要.
DO数据的非线性和非静态特征对传统预测模型构成挑战.

研究的目的:

开发和评估一种用于多步DO预测的新型混合模型.
通过利用数据分解和集体学习技术来提高DO预测的准确性.

主要方法:

用自适应噪声 (CEEMDAN) 完成集体实证模式分解,将DO数据分解为内在模式函数 (IMF).
混合预测方法:AdaBoost与双向长期短期内存 (BiLSTM) 用于高/中频IMF,以及独立的长期短期内存 (LSTM) 用于低频IMF.
模型验证使用来自甘河十个站的数据,与各种基线和分解模型进行比较.

主要成果:

提出的CEEMDAN-AdaBoost-BiLSTM-LSTM模型在DO预测方面表现出卓越的性能.
与CEEMDAN-BiLSTM相比,观察到显著的改善:25.458%的RMSE和37.390%的MAE减少.
还有进一步的改善:与CEEMDAN-AdaBoost-BiLSTM相比,减少了20.779%的RMSE和28.921%的MAE.
统计测试 (Diebold-Mariano和t-test) 证实了拟议模型的显著性能优势.

结论:

针对不同频率组件 (IMF) 量身定制的预测策略提高了预测准确度.
CEEMDAN-AdaBoost-BiLSTM-LSTM模型为多步DO预测提供了强大而准确的解决方案.
这种方法为优化水资源管理和环境监测提供了有价值的工具.

关键词:

在 AdaBoost 中使用 AdaBoost.完整的合奏实证模式分解与适应性噪声.长期短期记忆长期短期记忆预测水质预测水质

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