印度次大陆的相对湿度预测使用基于库马拉斯瓦米分布的非高斯模型
Mohammad Shad1, Yogeshver Dutt Sharma2, Pankaj Narula3
1Department of Mathematics and Scientific Computing, National Institute of Technology, Hamirpur, 177005, Himachal Pradesh, India. shadgkp@gmail.com.
Environmental science and pollution research international
|December 6, 2024
概括
本研究介绍了Kumaraswamy季节性自回归移动平均 (KSARMA) 模型,用于准确预测印度的相对湿度 (RH). 克萨尔马模型在印度大多数气候区都显示出卓越的预测准确度,有助于农业和天气预测.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 统计建模 统计建模
背景情况:
- 相对湿度 (RH) 显著影响农业,天气预报,室内空气质量和能源消耗.
- 由于其复杂的非线性行为,准确的RH估计具有挑战性.
- 准确的RH预测对于印度适应气候变化至关重要,特别是在农业和天气预测方面.
研究的目的:
- 提出和评估一个新的非高斯统计模型,Kumaraswamy季节性自回归移动平均 (KSARMA) 模型,用于RH预测.
- 评估KSARMA模型在印度五个不同的气候区的表现.
- 为了比较KSARMA模型的预测准确度与既有预测方法.
主要方法:
- 利用印度五个气候区的从1981年到2021年的月度RH数据.
- 采用诊断分析和Akaike的信息标准 (AIC) 进行模型选择.
- 使用条件最大概率估计器 (CMLE) 估计的模型参数.
主要成果:
- 与多层感知子,SARIMA,霍尔特-温特和SARMA模型相比,KSARMA模型显示出更高的预测准确度.
- 在温暖潮湿,温和,寒冷和复合气候区中观察到高精度.
- 包括RMSE (0.04-0.12),MAE (0.03-0.1),SMAPE (0.04-0.2) 和MASE (0.5-0.8) 在内的错误指标支持该模型的有效性.
结论:
- 该KSARMA模型提供了一个强大的和准确的方法,用于RH预测在不同的印度气候条件下.
- 这种先进的预测能力可以在气候变化场景下显著有利于农业规划和天气预测.
- 该研究强调了非高斯模型在改善气候相关预测方面的潜力.
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