使用人工智能技术对水质进行建模和预测,以加强监测和管理
S Barathkumar1, A Chitra Devi2
1Department of Soil Science and Agricultural Chemistry, Adhiyamaan College of Agriculture and Research, Krishnagiri, Tamil Nadu, India.
概括
这项研究引入了一个人工智能系统,用于实时监测和预测水质. 人工智能方法改善了水资源管理,并确保了水质标准的满足.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 水资源管理 水资源管理
背景情况:
- 供水和卫生系统需要具有成本效益的解决方案来确保质量合规.
- 实时监测和预测对于可持续的水资源管理和环境保护至关重要.
研究的目的:
- 开发和评估一种基于人工智能的新系统,用于实时监测和预测水质.
- 评估适应性回归和水质指数 (WQI) 和水质分类 (WQC) 的分类模型的性能.
主要方法:
- 利用自适应回归模型来预测水质指数 (WQI).
- 采用K-近邻 (KNN) 和随机森林 (RF) 等水质分类 (WQC) 的分类器.
- 在数据集中包括了10个基本的水质参数 (pH,EC,Ca2+,Mg2+,Na+,K+,CO3,HCO3,Cl,SO4) 在数据集中.
主要成果:
- 线性回归 (LR) 实现了最好的WQI预测 (MAE: 2.18,R2: 0.967).
- KNN和RF分类器显示WQC的高精度 (分别为92.3%和91.6%).
- 综合模型结合回归和分类,在WQI和WQC预测中实现了96.7%的回归系数.
结论:
- 由人工智能驱动的系统有效地提高了水质预测.
- 这些发现支持改善水处理,管理和决策,特别是在受污染影响的地区,如印度泰米尔纳德邦.
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