基于混合降噪的相对湿度的数据驱动建模在孟加拉国Khulna的相对湿度
Shuvendu Pal Shuvo1, Joarder MdAshikuzzaman1, Shirshendu Pal Shibazee2
1Department of Civil Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.
Heliyon
|September 10, 2024
概括
一个新的混合机器学习 (ML) 模型结合实证模式分解 (EMD) 和支持矢量机 (SVM) 显著提高了相对湿度 (RH) 预测的准确性. 这种方法有效地处理非线性和杂的数据,对于孟加拉国Khulna等城市化地区至关重要.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气象学 天气学
背景情况:
- 孟加拉国Khulna的快速城市化和环境变化导致了复杂和非线性相对湿度 (RH) 模式.
- 传统的预测技术在这种动态环境中扎,因为湿度数据的高度非线性和杂性.
研究的目的:
- 提出一种混合机器学习 (ML) 方法,将实证模式分解 (EMD) 与支持矢量机器 (SVM) 结合起来,以提高RH预测.
- 为了应对孟加拉国Khulna的非线性和杂的湿度数据所带来的挑战.
主要方法:
- 开发了一个混合模型,集成EMD用于数据预处理和SVM用于预测.
- 使用粒子集群优化 (PSO) 算法优化了SVM模型的超参数.
- 绩效使用MSE,RMSE,MAE,MAPE和R2等指标进行评估,数据分为培训,验证和测试集.
主要成果:
- 基于EMD的混合方法在预测准确度方面取得了显著的改进,各种模型配置的确定系数 (R2) 显著增加.
- 拟议的技术有效地过噪音,并处理高度非线性湿度数据,优于传统方法.
结论:
- 混合EMD-SVM模型为预测复杂环境条件下的相对湿度提供了强大而高效的解决方案.
- 建议将该方法应用于其他地质区域,这些地区在湿度数据预测方面面临着类似的挑战.
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