使用支持矢量机器 (SVM) 来预测伊拉克西部哈迪萨水库的流入
Othman A Mahmood1, Sadeq Oleiwi Sulaiman1, Dhiya Al-Jumeily2
1Dams and Water Resources Engineering Department, College Engineering, University of Anbar, Anbar, Iraq.
PloS one
|September 6, 2024
概括
使用支向量回归 (SVR) 准确预测伯拉河的流量可以提高水资源管理. 机器学习模型有效地预测每日河流排放量,帮助控制洪水和水库运营.
科学领域:
- 水文与水资源工程 水文与水资源工程
- 环境科学 环境科学
- 环境管理中的人工智能
背景情况:
- 准确的流入预测对于有效的洪水管理和优化供水系统至关重要.
- 水库的设计和运营策略在很大程度上依赖于精确的流入预测.
- 位于哈迪萨大上游的伯拉河是重要的水资源,需要可靠的流量预测.
研究的目的:
- 开发和通用化一种机器学习模型,用于预测伯拉河排水情况.
- 评估支持向量回归 (SVR) 的性能,使用不同的内核函数进行流入预测.
- 确定最佳的SVR模型配置,用于每日,每月和季节性流量预测.
主要方法:
- 利用了1986-2024年河流流量的时间序列数据.
- 应用支持向量回归 (SVR),一种机器学习技术.
- 测试了各种SVR内核函数:线性,二次性和高斯式 (辐射基函数 - RBF).
- 分析了每日,每月和季节性流量数据.
主要成果:
- 与月度和季节性规模相比,每日流量预测显示出更高的性能.
- 线性内核SVR模型,在1天的时间延迟下,实现了最高的准确性.
- 通过0.95的确定系数 (R2) 和每日流量为53.29 m3/sec的根平均平方误差 (RMSE) 验证了性能.
- 机器学习模型在预测每日河流流量方面被证明是有效的.
结论:
- 开发的机器学习模型显示,对准确的欧弗拉底河流入预测有很大的希望.
- 有效的每日流量预测可以大大提高水资源管理和水运营效率.
- 该研究强调了SVR在水文预测应用中的潜力.
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