一个月的温度预测基于CEEMDAN-BO-BiLSTM合模型
Xianqi Zhang1,2,3, He Ren4, Jiawen Liu1
1Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Scientific reports
|January 8, 2024
概括
准确的月度平均温度预测对于适应气候变化至关重要. 一个新的CEEMDAN-BO-BiLSTM模型在预测温度方面表现出卓越的准确性和适应性,优于现有方法.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候建模气候模型
背景情况:
- 准确的温度预测对于气候变化监测至关重要,影响农业,能源和灾害预警系统.
- 气候数据中的非线性和非平滑信号对传统预测模型构成挑战.
研究的目的:
- 开发一个强大而准确的月度平均温度预测模型.
- 利用CEEMDAN,BO算法和BiLSTM的优势,提高预测能力.
主要方法:
- 该研究整合了补充组合实证模式分解 (CEEMDAN) 进行信号分解.
- 贝叶斯优化 (BO) 算法用于目标函数优化.
- 一个双向长短期记忆 (BiLSTM) 网络被用于时间序列预测.
主要成果:
- 在CEEMDAN-BO-BiLSTM模型中,平均绝对误差为1.17,根平均平方误差为1.43.
- 与CEEMDAN-BiLSTM,EMD-BiLSTM和单独使用BiLSTM相比,该模型显示出更高的预测准确性和适应性.
- 弗里德曼的测试证实了模型的效率和缺乏过度建模.
结论:
- CEEMDAN-BO-BiLSTM模型是一个可行的和有效的方法,用于每月平均温度预测.
- 这种综合模型为与气候相关的应用提供了更好的预测性能.
- 这些发现为改善气候预测策略提供了宝贵的见解.
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