机器学习模型性能对比,用于预测马来西亚约翰霍尔巴鲁的气候变量
Farid Zamani Che Rose1,2, Nur Aqilah Khadijah Rosili3, Muhammad Fadhil Marsani4
1Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia. faridzamani@upm.edu.my.
Scientific reports
|July 2, 2025
概括
随机森林 (RF) 机器学习模型擅长预测هور巴鲁的温度和湿度等气候变量. 这项研究为当地气候适应战略提供了数据驱动的见解.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 准确的气候预测对于有效的空气质量管理和气候适应战略至关重要.
- 马来西亚的约霍尔巴鲁需要本地化的气候数据来告知公共政策和投资.
研究的目的:
- 评估五种机器学习模型 (SVR,RF,GBM,XGBoost,Prophet) 的性能,用于预测Johor Bahru的关键气候变量.
- 为当地利益相关者提供初步数据驱动的见解,以制定气候适应战略.
主要方法:
- 利用了来自NASA POWER的15888个每日时间序列气候数据,用于六个变量:温度 (T2M),露水/点 (T2MDEW),湿球温度 (T2MWET),特定湿度 (QV2M),相对湿度 (RH2M) 和降水 (PREC).
- 采用并比较了五种机器学习模型:支持向量回归 (SVR),随机森林 (RF),梯度增强机 (GBM),极端梯度增强机 (XGBoost) 和Prophet.
主要成果:
- 随机森林 (RF) 显示出卓越的预测性能,在训练和测试数据集中显示出最低的错误率.
- 对于与训练温度相关的变量 (T2M,T2MDEW,T2MWET),RF实现了90%以上的R2值,这表明其具有强大的预测能力.
- 支持向量回归 (SVR) 在测试阶段显示出更好的概括性,在样本外预测中达到0.88的最高Kling-Gupta效率 (KGE).
结论:
- 随机森林对于预测乔هور巴鲁的气候变量,特别是与温度相关的指标非常有效.
- 支持向量回归提供可靠的样本外预测能力.
- 该研究提供了有价值的,透明的,数据驱动的见解,以支持决策者制定气候适应策略和投资,以支持乔هور巴鲁.
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