在沙特阿拉伯的气候变化预测使用CNN GRU LSTM混合深度学习模型在al Qassim地区
Emad Elabd1,2, Hany Mohamed Hamouda1, M A Mohamed Ali3
1Department of Management Information Systems, College of Business and Economics, Qassim University, Buraidah, 51452, Qassim, Saudi Arabia.
Scientific reports
|May 9, 2025
概括
一种新的深度学习模型,即卷积神经网络门的重复单元长短记忆 (CNN-GRU-LSTM),准确地预测了气候变化趋势. 这种先进的模型优于传统方法,为环境政策和城市规划提供了重要的见解.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 气候变化对全球生态系统和城市地区构成重大威胁,并对经济产生重大影响.
- 准确预测气候变化影响对于有效的减缓和适应战略至关重要.
研究的目的:
- 开发和评估混合深度学习模型,用于预测气候变化指标.
- 对沙特阿拉伯Al-Qassim地区的传统回归技术进行模型性能评估.
主要方法:
- 采用了一种混合的卷积神经网络门的重复单元长短记忆 (CNN-GRU-LSTM) 深度学习架构.
- 用高斯噪声回归的合成少数人过量采样技术 (SMOGN) 来解决数据集失衡问题.
- 模型性能使用诸如平均平方误差 (MSE),平均绝对误差 (MAE),中位数绝对误差 (MedAE),根平均平方误差 (RMSE) 和R平方 (R2) 等指标进行评估.
主要成果:
- 与五种经典的回归模型 (DTR,RFR,ETR,BRR,K-近邻) 相比,CNN-GRU-LSTM模型表现出优异的性能.
- 该模型在四种气候场景中实现了高R平方值:99.62%,99.15%,99.71%和99.60%.
- 深度学习方法在预测温度,露点,可见度和海平面压力方面被证明是有效的.
结论:
- 深度学习模型,特别是CNN-GRU-LSTM架构,对于气候变化预测非常有效.
- 这些发现支持使用先进的人工智能来告知环境政策和指导城市发展决策.
- 准确的气候预测对于应对气候变化带来的挑战至关重要.
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