精确水质分类和WQI预测的先进机器学习模型:对水生病风险管理的影响
Md Abdullah Al Mamun Hridoy1, Abdullah Ibna Shawkat2, Chiara Bordin3
1Faculty of Fisheries, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
The Science of the total environment
|November 19, 2025
概括
准确的水质分类和水质指数 (WQI) 预测至关重要. 像XGBoost和LightGBM这样的机器学习模型显示出高精度,识别水生健康和疾病预防的关键因素.
科学领域:
- 环境科学 环境科学
- 水生生态学 水生生态学
- 数据科学数据科学数据科学
背景情况:
- 准确的水质评估对于水生生态系统和水产养殖健康至关重要.
- 预测水质指数 (WQI) 有助于有效管理水资源.
- 现有的方法在复杂的水生环境中可能缺乏精度.
研究的目的:
- 评估和比较各种机器学习模型的性能,用于水质分类和WQI预测.
- 确定影响水质的最有影响力的因素.
- 利用可解释的人工智能来解释模型决策.
主要方法:
- 对LightGBM,XGBoost,随机森林和支持矢量机器进行系统评估.
- 网格搜索优化用于增强模型性能.
- 对特征重要性和模型可解释性进行SHAP (夏普利添加式解释) 分析.
主要成果:
- 组合模型 (LightGBM,XGBoost) 在水质分类中实现了高精度 (高达99.65%).
- XGBoost回归显示出优异的WQI预测性能 (R2 = 0.9685).
- 溶解氧和BOD被确定为最重要的预测因素.
结论:
- 先进的机器学习模型为水质评估和WQI预测提供了高精度.
- 可解释的人工智能 (SHAP) 提供了对模型行为和影响水质参数的关键见解.
- 这些发现支持开发主动监测和早期预警系统,以实现可持续的水生生物健康管理.
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