使用机器学习和自适应性神经模糊推理系统的空气质量预测和控制系统
Pouya Mottahedin1, Benyamin Chahkandi2, Reza Moezzi3,4
1Department of Chemical Engineering, Faculty of Engineering, University of Garmsar, Garmsar, Iran.
这项研究使用人工智能预测伊朗的空气质量. 适应性神经模糊推理系统 (ANFIS) 证明在跨季节预测污染物度方面最有效.
科学领域:
- 环境工程环境工程
- 人工智能在环境科学中的应用.
- 空气质量监测和预测.
背景情况:
- 由于非线性污染物相互作用,空气质量预测是复杂的.
- 准确的预测对于环境管理和公共卫生至关重要.
- 现有的方法可能无法完全捕捉空气污染的动态性质.
研究的目的:
- 开发和评估用于伊朗塞姆南空气质量预测的人工智能模型.
- 确定最可靠的机器学习模型来预测污染物度.
- 为环境工程空气质量评估建立一个强大的框架.
主要方法:
- 收集和分析了七种不同的空气污染物的综合数据.
- 评估了几种机器学习 (ML) 模型的性能.
- 专注于自适应神经模糊推理系统 (ANFIS) 的预测能力.
主要成果:
- 适应性神经模糊推断系统 (ANFIS) 在空气质量预测方面表现出卓越的表现.
- 在各种数据集和季节中,ANFIS显示出高可靠性.
- 机器学习模型对预测准确性进行了严格评估.
结论:
- 安菲斯是研究区域季节性空气质量预测的高效工具.
- 人工智能为先进的环境工程解决方案提供了可靠的框架.
- 这些发现为伊朗空气质量管理提供了有价值的案例研究.
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