使用高效机器学习模型预测长江中部地区的水质
Jingjing Xia1, Wenya Chen2,3, Libing Liu2,3
1School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, People's Republic of China.
Environmental geochemistry and health
|October 31, 2025
概括
本研究介绍了长江的先进水质评估框架,将水质指数 (WQI) 和权WQI (EWQI) 与机器学习模型相结合,用于准确的实时监测和生态保护.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 长江的水质对于中国的生态安全和可持续发展至关重要.
- 实时监测中流域的水质对于公众健康和生态系统稳定至关重要.
研究的目的:
- 开发一个全面的框架,以实时评估长江的水质.
- 通过整合先进的机器学习技术来提高预测准确性.
主要方法:
- 同步的水质指数 (WQI) 与权WQI (EWQI).
- 应用Pearson相关性分析用于环境因素选.
- 使用支持矢量机器 (SVM),多输出SVM (MSVM) 和灰狼优化器 (GWO) 进行预测建模.
主要成果:
- 该GWO-MSVM模型显示出卓越的预测性能,最大RMSE为1.40.
- 达到0.92的最小确定系数 (R2) 和1.04的最大平均绝对误差 (MAE).
- 优化的模型显著提高了水质预测的效率和准确性.
结论:
- 拟议的GWO-MSVM模型为水质评估提供了可靠的技术框架.
- 这项研究为长江流域的生态保护提供了战略支持.
- 综合方法提高了实时水质监测和管理的能力.
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