实施机器学习方法,对中央爪的地表水质进行深入分析和分类
Valentine Conny Putri Perdana1, Suherman Suherman1, Darly Guntur Darris Purba2
1Diponegoro University: Universitas Diponegoro, Semarang, Central Java, Indonesia.
Environmental science and pollution research international
|November 3, 2025
概括
机器学习模型准确地预测了水污染,但一些"安全"的分类违反了法律标准. 提出了一个监管意识层,以确保人工智能驱动的环境监测与合规性保持一致.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 水质监测对于环境保护和公共卫生至关重要.
- 生态挑战和可持续的资源管理需要先进的监测解决方案.
- 机器学习为准确的水污染评估提供了潜力.
研究的目的:
- 提出和评估用于水污染分类的机器学习框架.
- 为了比较支持向量机 (SVM) 和极端梯度增强 (XGBoost) 模型的性能.
- 将监管合规验证纳入预测框架.
主要方法:
- 使用支持向量机 (SVM) 和极端梯度提升 (XGBoost) 进行预测分类.
- 为了解决数据不平衡,采用合成少数群体过量采样技术 (SMOTE-Tomek).
- 使用SHAP和LIME进行了可解释性分析,并根据印度尼西亚政府第1号法规进行了验证. 2021年22月22日. 这是2021年22月22日.
主要成果:
- XGBoost实现了98.76%的准确率和97.62%的F1-宏积分;SVM实现了90.25%的准确率和83.57%的F1-宏积分.
- 生物和化学指标 (便大肠杆菌,BOD,COD) 显示特征的重要性很高.
- 模型预测确定了违反监管门的潜在"假安全"分类.
结论:
- 机器学习模型对水污染水平的预测准确度很高.
- 确保监管合规性至关重要,因为模型可能产生"假安全"结果.
- 建议采用基于规则的验证和校准的监管意识层,以提高现实世界的适用性和政策调整.
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