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基于加权内核极端学习机器和回归的新型建模框架的开发,用于流量预测
Arvin Samadi-Koucheksaraee1, Xuefeng Chu2
1Department of Civil, Construction and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND, 58108-6050, USA.
Scientific reports
|December 27, 2024
概括
本研究介绍了WKELM-R,这是一种用于精确流量预测的新型混合机器学习模型. 该模型有效地预测了跨多个时间地平线的流量,超过了现有的方法.
科学领域:
- 水文学的水文学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 准确的流量预测对于水资源管理和防灾准备至关重要.
- 现有的机器学习模型在预测器优化,跨时间范围的概括和高维数据分析方面面临挑战.
研究的目的:
- 开发一种新的混合机器学习框架,WKELM-R,用于增强流量预测.
- 为了解决预测器选择,模型概括和处理复杂水文时间序列的局限性.
主要方法:
- 开发了一个混合模型 (WKELM-R),结合了回归,局部加权线性回归和内核极端学习机器.
- 数据预处理涉及多变量变化模式分解 (MVMD) 进行非静止,Boruta-XGBoost用于特征选择,以及基于梯度的优化器 (GBO) 进行参数调整.
- 该模型应用于北达科他州的一个流域,用于多步前进的溪流预测.
主要成果:
- WKELM-R模型在多个时间视界的流量预测中显示出高准确度 (例如,R=0.992,RMSE=0.426在t+3;R=0.997,RMSE=0.249在t+7;R=0.996,RMSE=0.304在t+14).
- 使用多标准决策 (MCDM) 对现有模型进行性能验证.
- 该模型有效地处理了非静止性,选择了最佳预测因素,并优化了参数.
结论:
- 拟议的WKELM-R框架为流量预测提供了一个强大而有效的解决方案.
- 混合方法成功地整合了线性和非线性动态,以提高预测准确度.
- 这项研究通过克服传统机器学习方法的关键局限性,提高了水文预测能力.
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