使用深度卷积神经网络和长短期记忆力的月度气候预测
Qingchun Guo1,2,3,4, Zhenfang He5,6, Zhaosheng Wang7
1School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China. guoqingchun@lcu.edu.cn.
Scientific reports
|July 31, 2024
概括
人工智能模型,包括混合CNN-LSTM,预测济南市的气候参数. 该CNN-LSTM模型显著提高了预测月度气候因素的准确性,有助于预防灾害.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 气候建模气候模型
背景情况:
- 气候变化对生态系统,农业和人类福祉构成重大威胁.
- 准确的气候参数预测对于缓解和适应战略至关重要.
- 济南市72年 (1951-2022) 的气候数据为分析提供了坚实的基础.
研究的目的:
- 评估各种人工智能 (AI) 模型在模拟和预测月度气候参数方面的有效性.
- 为了比较人工神经网络 (ANN),循环神经网络 (RNN),长期短期记忆 (LSTM),卷积神经网络 (CNN) 和混合CNN-LSTM模型的预测准确度.
- 确定在济南市准确预测气候的最有效的人工智能模型.
主要方法:
- 利用了济南市72年的月度气候数据 (温度,降水,湿度,日照时间).
- 用时间序列数据与12个月的延迟作为人工智能模型的输入.
- 通过使用平均绝对误差,根平均平方误差 (RMSE) 和相关系数 (R) 来比较ANN,RNN,LSTM,CNN和CNN-LSTM模型.
主要成果:
- 混合CNN-LSTM模型在预测月度气候参数方面表现出优异的准确性,与单独的ANN,RNN,LSTM和CNN模型相比.
- 在月平均大气温度 (0.6292°C) 中,CNN-LSTM模型实现了最低的RMSE,显著优于其他模型.
- 提出的模型,特别是CNN-LSTM,显示了提高气候预测精度的巨大潜力.
结论:
- 混合CNN-LSTM模型在气候预测准确性方面取得了重大进展.
- 改进的气候预测能力可以加强气象灾害预防,洪水控制和抗旱工作.
- 人工智能驱动的气候模拟对于可持续发展和保护人类健康免受气候变化影响至关重要.
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