用CNN-LSTM混合模型预测历史气温，提升预报准确性和稳定性。
Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data
- CNN提取空间特征，LSTM捕捉时间依赖性
- MAE损失函数下预测曲线与实测数据高度吻合
- 适合气候预测、农业与能源管理领域应用
随着全球气候变化加剧，精准天气预报在农业、能源管理、环境保护和日常生活中愈发重要。本文提出一种结合卷积神经网络（CNN）与长短期记忆网络（LSTM）的混合模型，用于预测历史气温数据。其中CNN负责空间特征提取，LSTM处理时间序列依赖关系，显著提升了预测精度与稳定性。采用平均绝对误差（MAE）作为损失函数，模型在处理复杂气象数据时表现优异，有效应对缺失数据与高维挑战。实验结果表明，预测曲线与测试数据高度一致，验证了该模型在气候预测中的潜力。研究为农业、能源管理及城市规划等领域提供了重要参考，也为全球气候变化背景下的天气预报应用奠定了基础。
原文摘要 · Abstract (English)
As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. CNNs are utilized for spatial feature extraction, while LSTMs handle temporal dependencies, resulting in significantly improved prediction accuracy and stability. By using Mean Absolute Error (MAE) as the loss function, the model demonstrates excellent performance in processing complex meteorological data, addressing challenges such as missing data and high-dimensionality. The results show a strong alignment between the prediction curve and test data, validating the model's potential in climate prediction. This study offers valuable insights for fields such as agriculture, energy management, and urban planning, and lays the groundwork for future applications in weather forecasting under the context of global climate change.
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