用多尺度神经网络精准预测中国东部气温变化
Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model
- 融合卷积、LSTM与注意力机制，捕捉气温时空特征
- 测试集上MSE为1.978，RMSE达0.811，精度显著提升
- 适合气象预报、农业与能源管理等实际决策场景
近年来，受全球气候变化和数据科学快速发展影响，精准天气预报的重要性日益凸显。传统方法难以应对气候数据固有的复杂性和非线性问题。为此，本文提出一种基于多尺度卷积-循环-注意力架构的气象预测模型，专门用于中国气温时间序列预测。该模型结合卷积神经网络（CNN）的空间特征提取能力、长短期记忆网络（LSTM）的时序建模优势以及注意力机制对关键信息的聚焦能力。模型开发涵盖数据采集、预处理、特征提取与建模全过程。实验结果表明，该模型在气温趋势预测中表现优异，最终计算结果显示均方误差（MSE）为1.978295，均方根误差（RMSE）为0.8106562。本研究标志着深度学习技术在气象数据应用中的重要进展，为提升天气预报精度提供了有力工具，并在城市规划、农业和能源管理等领域提供关键决策支持。
原文摘要 · Abstract (English)
In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to handle the complexity and nonlinearity inherent in climate data. To address these challenges, we propose a weather prediction model based on a multi-scale convolutional CNN-LSTM-Attention architecture, specifically designed for time series forecasting of temperature data in China. The model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and attention mechanisms to leverage the strengths of spatial feature extraction, temporal sequence modeling, and the ability to focus on important features. The development process of the model includes data collection, preprocessing, feature extraction, and model building. Experimental results show that the model performs excellently in predicting temperature trends with high accuracy. The final computed results indicate that the Mean Squared Error (MSE) is 1.978295 and the Root Mean Squared Error (RMSE) is 0.8106562. This work marks a significant advancement in applying deep learning techniques to meteorological data, offering a valuable tool for improving weather forecasting accuracy and providing essential support for decision-making in areas such as urban planning, agriculture, and energy management.
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