长途供水项目中水质的时空空间演变:改进的PSO-SVR模型
Haidong Yang1, Ting Zou1, Yilin Huang1
1School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.
Environmental monitoring and assessment
|November 18, 2025
概括
一种名为IPSO-SVR的新方法准确地模拟了远距离输水项目 (LWTP) 中的水质变化. 这种先进的机器学习方法可以提高预测准确度和可靠性,从而更好地管理水资源.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 空间时空水质的演变对于管理远距离输水项目 (LWTP) 至关重要.
- 水质数据具有多维和非线性特征,对准确的监测和预测构成挑战.
- 有效地管理LWTP需要强大的方法来理解和预测水质动态.
研究的目的:
- 提出一种用于描绘LWTP水质的时空演变的新方法.
- 通过主要成分分析 (PCA) 确定关键的水质指数.
- 开发和验证一个改进的机器学习模型 (IPSO-SVR) 以提高水质特征.
主要方法:
- 主要成分分析 (PCA) 用于确定有影响力的水质指数.
- 开发了一种改进的支持向量回归 (SVR) 模型,IPSO-SVR,集成粒子集群优化 (PSO) 和K折交叉验证.
- IPSO-SVR模型使用来自南向北水分转移项目的真实水质数据进行了验证.
主要成果:
- 与SVR,PCA-SVR,PSO-SVR,随机森林,XGBoost和线性回归相比,IPSO-SVR模型显示出更高的性能.
- IPSO-SVR提高了0.9%6.5%的确定系数 (R2),并将平均平方误差 (MSE) 降低了17%52%.
- 拟议的方法显示在水质预测中增强了稳定性,可靠性和适用性.
结论:
- IPSO-SVR方法提供了一种新且有效的方法,用于分析LWTPs的时空水质演变.
- 这项研究为改善中大型和大型水转移项目的水质管理提供了有价值的工具.
- 这些发现强调了先进的机器学习技术在解决复杂的环境监测挑战方面的潜力.
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