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基于改进的粘液模具算法优化SVR的河流和湖泊生态系统健康智能诊断方法
Ran Chi1, Yuewu Da2, Weiying Li1
1College of Environmental Science and Engineering, Tongji University, Shanghai, China.
PloS one
|January 21, 2026
概括
这项研究引入了用于诊断河流和湖泊生态系统的智能模型,提高了水质预测准确度. 先进的模型提供了更好的早期预警系统,用于生态风险预防和控制.
科学领域:
- 环境科学 环境科学
- 生态生态学 生态生态学
- 数据科学数据科学数据科学
背景情况:
- 复杂的非线性关系和水质参数的动态变化挑战了生态系统诊断的传统统计方法.
- 现有方法的准确性,及时性和适用性的局限性阻碍了精确的评估和早期预警系统.
研究的目的:
- 为河流和湖泊生态系统开发智能诊断模型,克服传统统计分析的局限性.
- 提高水质预测和生态系统健康诊断的准确性,及时性和适用性.
主要方法:
- 开发了一个改进的粘液模具算法优化的支向量回归模型,具有动态重量策略和自适应性搜索机制.
- 集成了LightGBM特征选择,以构建一个组合模型,解决高维数据建模和动态适应性.
- 模型的性能使用诸如根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R平方 (R2) 等指标进行评估.
主要成果:
- 优化的模型显示了比较模型的显著改进,RMSE为0.031,MAE为0.021,R2为0.942.
- 最终的组合模型实现了优异的预测准确性,RMSE为0.022,MAE为0.016,R2为0.976.
- 该模型对包括pH值,溶解氧和化学氧气需求在内的关键水质参数产生了良好的预测效应,对异常值的敏感性较低.
结论:
- 拟议的智能诊断模型为水质预测和河流和湖泊生态系统健康诊断提供了有效和准确的方法.
- 该研究的结果通过加强生态风险预防和控制能力,支持水环境管理的数字化转型.
- 开发的模型表现出卓越的性能和实际预测有效性,用于诊断河流和湖泊生态系统.
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