使用水质指数,多变量技术和可解释的机器学习模型,对影响饮用水源和卫生设施的地表水质量评估在印度奥迪沙州的马哈纳迪河
1Department of Civil Engineering, C.V. Raman Global University (CGU), Bhubaneswar, Odisha, India. abhijeetlaltu1994@gmail.com.
Environmental geochemistry and health
|October 14, 2025
概括
这项研究使用水质指数 (WQI) 和机器学习评估了马哈纳迪河流域的水质. 结果显示,水质各不相同,度和人为因素影响了适合饮用和农业的水质.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 水资源管理 水资源管理
背景情况:
- 地表水质量对于作物生产率和饮用水需求的增加至关重要.
- 评估水化学特征对于管理大哈纳迪河流域的水资源至关重要.
研究的目的:
- 评估地表水质量及其适合饮用和农业用途.
- 用各种统计和机器学习模型预测水质指数 (WQI),用于可持续的水资源管理.
主要方法:
- 使用的加权算术水质指数 (WAWQI),皮尔森相关性,集群分析 (CA) 和主要组件分析 (PCA).
- 利用了六种机器学习技术,包括高斯过程回归 (GPR),线性回归,人工神经网络 (ANN),支持矢量机器 (SVM) 和合适的二进制树 (FBT) 进行WQI预测.
- 从11个地表水样本中分析了13个物理化学参数.
主要成果:
- 确定了Mg2+ > Ca2+ > K+ > Na+和HCO3- > Cl- > SO42- > NO3-作为初级离子和离子度.
- WAWQI显示,水质从优秀到不合适,贫困和非常贫困类别占主导地位 (分别为27.27%).
- 度,碳酸气候变化和农业/城市工业增长被确定为水质变化的关键调节因素. GPR,渐进线性回归和ANN模型在WQI预测中表现出卓越的性能.
结论:
- 综合方法结合了物理化学分析,WQI,多变量统计和机器学习,有效地评估了地表水的适用性,并确定了调节因素.
- 研究结果强调了人为活动和自然过程 (如气候变化) 对马哈纳迪河流域水质的重大影响.
- 建议提高数据准确性,并扩大模型适用于不同的地理环境,以加强水资源管理.
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