基于CEEMDAN,LMD和ANN混合的短期和长期天气预报
Samuel Asante Gyamerah1, Victor Owusu2
1Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
PloS one
|July 22, 2024
概括
准确的天气预报对于面临气候变化的非洲农业至关重要. 结合CEEMDAN,LMD和NN的新混合模型改善了农民和保险公司的气候预测.
科学领域:
- 农业科学 农业科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 非洲农业严重依赖气候,因此容易受到极端天气事件的影响.
- 气候变化对农业生产产生负面影响,威胁到粮食安全和经济稳定.
- 准确的天气预报对于农民来说至关重要,以适应做法并减轻与气候有关的损害.
研究的目的:
- 开发一种先进的天气预报模型,以在极端气候变化中提高准确性.
- 通过可靠的多阶段天气预报,增强农业利益相关者的决策系统.
主要方法:
- 提出了一种新的混合预测模型,将完整集体实证模式分解与适应噪声 (CEEMDAN),局部平均分解 (LMD) 和人工神经网络 (NN) 集成在一起.
- 通过对短期和长期预测能力的详细比较指标来评估模型的性能.
- 将混合模型与现有预测模型进行基准测试,以确定其有效性.
主要成果:
- 三相混合CEEMDAN-LMD-NN模型在关键评估指标上显示出最佳性能.
- 该模型在生成可靠的多步视界预测方面被证明是有效的.
- 对比分析证实了拟议的混合模型优于其他预测技术的优势.
结论:
- CEEMDAN-LMD-NN模型为气候脆弱的农业部门的天气预报提供了显著的进步.
- 该模型的效率支持商业农民,小农和农业保险行业的决策系统设计.
- 可靠的长期天气预报对于非洲农业适应气候变化至关重要.
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