混合FCMG-OP-FIS模型方法将回归转换为分类数据,用于基于机器学习的AQI预测
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai, Chennai, 600127, Tamilnadu, India.
Heliyon
|November 11, 2024
概括
这项研究引入了一种新的方法,通过结合模糊逻辑和机器学习来预测空气质量指数 (AQI). 该方法准确地转换数据进行分类,改善城市空气污染监测.
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:
- 来自各种来源的城市空气污染对健康构成重大风险.
- 有效的空气质量指数 (AQI) 监测对公共卫生至关重要.
- 现有的机器学习 (ML) 方法在 AQI 预测的回归到分类数据转换方面遇到了困难.
研究的目的:
- 开发一种使用综合模糊逻辑和ML方法准确预测AQI的新方法.
- 解决传统的ML技术在处理回归数据集等级方面的局限性.
- 通过结合污染物和气象数据来改善AQI预测.
主要方法:
- 将模糊中心合并图与基于最佳值的模糊推理系统 (FCMG-OP-FIS) 集成用于数据转换.
- 使用FCMG来平衡数据集和规则管理的组输入.
- 使用IF-THEN规则将FCMG-OP-FIS回归输出分类为AQI水平 (健康,中等,不健康).
- 为了提高准确性,对机密数据进行随机森林分类人员 (RFC) 的培训.
主要成果:
- FCMG-OP-FIS模型成功地将回归数据转换为分类框架.
- 回归输出验证显示出较低的错误率 (RMSE:0.48,MSE:0.23,MAE:0.23,MAPE:1.77%). 这是一个非常好的方法.
- 通过使用先进的验证技术,RFC模型实现了99%的准确性,F1得分,精度和回忆率也达到99%.
结论:
- 拟议的FCMG-OP-FIS和RFC模型有效地预测高精度的AQI.
- 这种方法证明了对分类任务的数据标签的卓越能力.
- 该模型显示了环境监测和环境管理系统中实际应用的巨大潜力.
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