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将果和火优化算法与支持矢量回归集成在估计每日面蒸发量的过程中
Milad Sharafi1, Saeed Samadianfard2, Javad Behmanesh3
1Department of Water Engineering, Urmia University, Urmia, Iran.
International journal of biometeorology
|December 7, 2023
概括
果算法 (FOA) 显著提高了支持向量回归 (SVR) 预测面蒸发. 与SVR和SVR-FFA相比,SVR-FOA组合模型显示出更高的准确性,使其成为水文预测的理想选择.
科学领域:
- 水文和气象学
- 机器学习应用 机器学习应用
背景情况:
- 泛蒸发是水资源管理和农业规划的关键因素.
- 准确预测面蒸发对于灌计划和干旱评估至关重要.
研究的目的:
- 使用支持向量回归 (SVR),SVR与果算法 (SVR-FOA) 结合,SVR与火算法 (SVR-FFA) 结合.
- 评估这些模型在伊朗西阿塞拜疆的四个站点进行泛蒸发估计的性能.
主要方法:
- 收集了1990-2020年的气象数据.
- 用皮尔森相关系数来确定重要的输入参数 (风速和阳光小时).
- 开发和比较了SVR,SVR-FOA和SVR-FFA模型,以预测泛蒸发.
主要成果:
- 风速和日照时间被确定为蒸发的最有影响力的参数.
- 在乌尔米亚站,SVR-FFA表现最好.
- 在其他站点上,SVR-FOA的表现优于SVR和SVR-FFA,错误率最低.
结论:
- 果算法 (FOA) 提供了诸如简单性和效率等优势,显著提高了SVR模型的能力.
- 建议使用SVR-FOA作为研究区域面蒸发估计的最准确和最有效的方法.
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