一种基于数据驱动的饮用水质量风险评估新方法
Jin Wu1, Tianyi Zhang2, Haibo Chu3
1Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China. d40614012@163.com.
Environmental geochemistry and health
|May 21, 2025
概括
一个新的模糊数据驱动的水质指数 (FDWQI) 有效地评估了各种水类的饮用水风险. 这种先进的方法克服了传统的WQI限制,识别了当地地表和地下水源的重大风险.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 水质风险评估对于可持续的水资源管理至关重要.
- 传统的水质指数 (WQI) 方法与主观性和不确定性作斗争.
- 对于各种水类的统一框架仍然是一个挑战.
研究的目的:
- 开发一个改进的饮用水质量风险评估框架.
- 整合机器学习,模糊数学和WQI以提高准确性.
- 解决传统WQI在处理不确定性和模糊性的局限性.
主要方法:
- 提出了一个基于模糊数据的水质指数 (FDWQI) 模型.
- 集成机器学习,综合权重和模糊数学.
- 使用梯形成员函数进行参数分类 (理想的,好的,坏的).
- 基于污染风险和数据集选择波动性的雇员指数选.
主要成果:
- 在不同的水类型中,FDWQI模型表现出高精度 (高AUC和精度).
- 与传统的WQI相比,FDWQI提供了更准确,合理和可解释的预测.
- 在73%的地表水,7%的浅地下水和21%的深地下水中确定了高水质风险.
- 地表水表现出极其严重的风险,使其不适合饮用.
结论:
- 饮用水质量风险评估FDWQI提供了一种强大且可适应的饮用水质量风险评估方法.
- 这种方法有效地处理了水质评估中固有的主观性和不确定性.
- 该研究强调了该地区的关键水质问题,强调了有针对性的干预措施的必要性.
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