通过基于标准化降水指数的EEMD-ARIMA建模,提高干旱预测精度
1Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Malaysia
概括
这项研究使用集体实证模式分解 (EEMD) 与自回归集成移动平均 (ARIMA) 模型来增强干旱预测. 在降水数据的传统ARIMA方法上,EEMD-ARIMA方法显著提高了预测准确性.
科学领域:
- 水文和气候科学 水文和气候科学
- 时间序列分析时间序列分析
- 环境监测 环境监测
背景情况:
- 干旱预测对于水资源管理和防灾准备至关重要.
- 传统的预测模型经常与气候数据的复杂,非线性性质作斗争.
- 准确的干旱预测需要先进的分析技术来捕捉潜在的模式.
研究的目的:
- 为干旱预测引入和评估一种新的集体实证模式分解与自回归集成移动平均值 (EEMD-ARIMA) 模型.
- 为了比较EEMD-ARIMA模型与传统的自回归集成移动平均 (ARIMA) 模型的性能.
- 评估标准化降水指数 (SPI) 在多个时间尺度上的预测准确性.
主要方法:
- 分析了阿富汗赫拉特省 (1970-2019) 的每月降水数据.
- 标准化降水指数 (SPI) 计算为3,6,9和12个月的时间表.
- 集体实证模式分解 (EEMD) 用于将SPI系列分解为内在模式函数 (IMF) 和残余.
- 自动回归集成移动平均线 (ARIMA) 模型被用于预测每个IMF和剩余.
- 通过总结单个组件的预测,生成了一个整体预测.
主要成果:
- 与传统的ARIMA模型相比,EEMD-ARIMA模型的干旱预测准确度明显更高.
- 统计指标包括根-平均-平方误差,平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和R平方证实了EEMD-ARIMA方法的优越性.
- 在所有评估的SPI时间尺度 (SPI 3,SPI 6,SPI 9,SPI 12) 中观察到更好的预测性能.
结论:
- EEMD与ARIMA的整合为干旱预测提供了一个强大而准确的方法.
- EEMD-ARIMA模型有效地捕捉复杂的数据特征,从而提高了预测能力.
- 这种先进的方法为改善水资源管理和减轻脆弱地区干旱影响提供了有价值的工具.
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