应用经验模式分解,粒子群优化和支持矢量机方法来预测流量流动
Okan Mert Katipoğlu1, Sefa Nur Yeşilyurt2, Hüseyin Yıldırım Dalkılıç2
1Department of Civil Engineering, Faculty of Engineering Architecture, Erzincan Binali Yıldırım University, Erzincan, Turkey. okatipoglu@erzincan.edu.tr.
Environmental monitoring and assessment
|August 29, 2023
概括
与单个模型相比,EMD-PSO-LSSVM混合模型在月度流量建模中表现出优异的性能. 经验模式分解 (EMD) 增强了模型准确性,对水资源管理至关重要.
科学领域:
- 水文学的水文学
- 水资源管理 水资源管理
- 计算智能是一种计算智能.
背景情况:
- 精确的溪流流量建模对于水资源规划,洪水控制和干旱管理至关重要.
- 混合模型为改善水文预测提供了潜力.
研究的目的:
- 评估混合模型的性能,结合最小方位支向量机 (LSSVM),实证模式分解 (EMD) 和粒子群集优化 (PSO) 来进行每月流量建模.
- 确定最佳的模型配置和输入变量,用于在半干旱地区的流量预测.
主要方法:
- 利用了42年的每月平均流量数据,这些数据来自于科尼亚封闭盆地的两个站 (1964-2005).
- 采用LSSVM,EMD和PSO技术来构建混合模型.
- 选择基于部分自相关的滞后流量值作为模型输入.
- 使用MSE,RMSE和相关系数等指标评估模型性能,用泰勒和小提琴图可视化.
主要成果:
- 混合EMD-PSO-LSSVM模型的表现优于单个的LSSVM,PSO-LSSVM和EMD-LSSVM模型.
- 根据相关系数R,EMD将LSSVM和PSO-LSSVM模型的性能提高了1-5% (基于相关系数R).
- 半干旱地区的最佳输入组合包括1个,9个,10个,11个和12个月的滞后流量值.
结论:
- 混合型EMD-PSO-LSSVM模型在半干旱的气候中对每月流量流量进行建模是非常有效的.
- 经验模式分解显著提高了基于LSSVM的模型的预测准确度.
- 该研究为优化水文预测系统提供了宝贵的见解.
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