一个新的alpha-logarithmic生成类,用理论和推理来建模降水数据
1Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Heliyon
|October 9, 2023
概括
一个新的alpha-logarithmic-generated (NAL-G) 分布类,包括NAL-Exponential (NAL-Exp) 模型,提供优越的降雨预测. 这种创新方法提高了水文建模的准确性,与传统方法相比.
科学领域:
- 水文学和统计建模.
- 开发用于气象数据分析的新型概率分布.
背景情况:
- 降水建模对于地球系统和生命可持续性至关重要.
- 现有的统计分布往往无法准确地捕捉复杂的降雨模式.
研究的目的:
- 介绍新的阿尔法对数生成 (NAL-G) 分布类.
- 评估NAL-Exp分布用于降水建模的数学特性和性能.
主要方法:
- 对NAL-G类属性的数学分析 (时刻,量子函数,,顺序统计).
- 应用和模拟六种经典估计方法.
- 使用四个现实世界降雨数据集进行比较性能分析.
主要成果:
- NAL-Exp分布在模拟降雨数据方面表现出高适应性和卓越性能.
- 在准确性和可靠性方面优于常用的分布模型.
- 通过广泛的模拟,确定了最有效的估计方法.
结论:
- NAL-Exp分布为水文建模提供了重要的进步.
- 该NAL-G类为增强降雨预测提供了多功能和灵活的框架.
- 这种新的方法有望提高未来气象预报的准确性和可靠性.
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