Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data

TL;DR

This paper presents a CNN-LSTM hybrid model that effectively predicts historical temperature data, improving accuracy and stability for weather forecasting amid climate change challenges.

Contribution

Introduces a novel CNN-LSTM hybrid model specifically designed for temperature prediction, combining spatial and temporal features for enhanced accuracy.

Findings

Model achieves low MAE on test data

Strong correlation between predictions and actual temperatures

Addresses missing data and high-dimensional meteorological data

Abstract

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.