使用模糊推理系统方法评估饮用水质量:罗马 (意大利) 的案例研究
Yas Barzegar1, Irina Gorelova1, Francesco Bellini1
1Department of Management, Sapienza University of Rome, 00161 Rome, Italy.
概括
这项研究引入了一个分层模糊模型来预测意大利罗马的饮用水质量. 人工智能方法通过分析关键参数和管理系统不确定性,有效评估水安全.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 环境监测中的人工智能
背景情况:
- 确保安全的饮用水对于公共卫生和社会福祉至关重要.
- 预测模型可以增强水资源管理和污染控制策略.
- 人工智能 (AI) 方法在水质评估方面显示出显著的前景.
研究的目的:
- 开发和评估一个层次的模糊模型来预测意大利罗马的饮用水质量.
- 评估Mamdani模糊推断系统在处理水质数据中的有效性.
- 为复杂的水质评估挑战提供可适应的AI解决方案.
主要方法:
- 一个层次的模糊模型,采用Mamdani模糊推理系统与各种模糊化技术.
- 该模型包括三个中间模糊模型和一个最终模糊模型,每个模型都有三个输入参数.
- 使用了包括9个水质参数 (性,硬度,pH,Ca,Mg,化物,硫酸盐,酸盐,铁) 的数据集.
主要成果:
- 基于模糊逻辑的模型在解决水质评估中固有的不确定性和主观性方面表现出有效性.
- 提出的分层模糊模型成功地根据所选参数预测了饮用水质量.
- 该系统的基于规则的结构 (每个模型有27个模糊规则) 促进了对水质指标的全面分析.
结论:
- 模糊逻辑为管理复杂和不确定的水系统提供了适当和有效的方法.
- 开发的分层模糊模型为饮用水质量预测提供了强大的解决方案.
- 该模型的模块化设计允许在未来的应用中轻松修改和潜在的性能改进.
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