优化机器学习模型用于印度主要城市的空气质量指数预测
Suresh Kumar Natarajan1, Prakash Shanmurthy2, Daniel Arockiam3
1School of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, Karnataka, India.
Scientific reports
|March 22, 2024
概括
这项研究介绍了一种优化的人工智能模型,用于预测印度主要城市的空气质量指数 (AQI). 灰狼优化决策树模型显著提高了空气污染预测的准确性.
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:
- 城市空气污染,由工业化和车辆排放驱动,大幅提高大城市的空气质量指数 (AQI).
- 准确的AQI分析对于实施有效的政府措施来缓解空气污染至关重要.
- 现有的AQI预测方法需要改进,以满足快速城市化地区的需求.
研究的目的:
- 在印度主要城市开发和验证一个优化的人工智能模型,用于精确的空气质量指数 (AQI) 预测.
- 通过整合先进的机器学习技术,提高空气污染预测的准确性.
- 为决策者提供可靠的工具,以应对城市空气质量挑战.
主要方法:
- 开发了一个优化的机器学习模型,将灰狼优化 (GWO) 与决策树 (DT) 算法结合起来.
- 该模型使用Kaggle的空气质量数据进行了训练和测试,用于印度主要城市,包括德里,海德拉巴,加尔各答,班加罗尔,维萨卡帕特南和奈.
- 性能使用R-Square,Root Mean Square Error (RMSE),Mean Squared Error (MSE),Mean Absolute Error (MAE) 和准确度等指标进行评估,并与k-nearest Neighbors,随机森林和支持向量回归模型进行比较.
主要成果:
- 拟议的GWO-DT模型与传统的机器学习算法相比,显示出更高的预测性能.
- 实现了高准确率:新德里为88.98%,班加罗尔为91.49%,加尔各答为94.48%,海德拉巴为97.66%,奈为95.22%,维萨卡帕特南为97.68%.
- 该模型在各种主要城市预测AQI的有效性经过实验验证.
结论:
- 优化的GWO-DT模型在人工智能驱动的空气质量预测方面取得了重大进展.
- 这种方法为预测AQI提供了更准确,更可靠的方法,有助于污染控制策略.
- 该研究强调了混合人工智能模型在解决城市空气污染等关键环境问题的潜力.
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