基于鱼优化算法 (OOA) 的湖泊污染物预测研究
Yinshan Yu1, Xu Tang2, Mingjian Ding2
1Jiangsu Engineering Research Center of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian, China. yuyinshan@163.com.
Analytical methods : advancing methods and applications
|August 8, 2025
概括
本研究引入了一种 Osprey 优化算法 (OOA),以增强高斯过程回归 (GPR) 以准确预测湖泊污染物. 对OOA优化的模型显著提高了对水质参数的预测准确度.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 算法优化的算法优化
背景情况:
- 准确测量湖水质量参数对于环境监测至关重要.
- 在测量参数和污染物度之间存在复杂的非线性关系.
- 现有的预测模型可能会受到低于最佳准确性的影响.
研究的目的:
- 开发一种使用优化算法预测湖泊污染物的增强方法.
- 提高高斯过程回归 (GPR) 的准确性和适应性,用于污染物预测.
- 完善实时评估水质的方法方法.
主要方法:
- 使用光谱光度测量测量六个水质参数 (COD,总,总).
- 用高斯过程回归 (GPR) 来预测污染物度的应用.
- 使用Osprey优化算法 (OOA) 优化GPR超参数.
主要成果:
- 经过OOA增强的GPR模型实现了高预测准确度.
- 在训练和测试数据集中,相关系数 (R^2) 超过了0.9.
- 平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 的指标接近零.
结论:
- Osprey优化算法有效地改进了用于污染物预测的GPR模型.
- 改进后的模型在湖泊污染物预测方面表现出卓越的性能.
- 这种方法为复杂的水质参数测量挑战提供了强大的解决方案.
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