Research on Global Climate Change Prediction based on Machine Learning Model

Abstract

Climate prediction models have traditionally relied on complex physical equations to simulate the dynamics of the climate system, but these models often require significant computational resources and long computational lengths. In recent years, machine learning techniques have shown great potential for pattern recognition and prediction. Specifically, machine learning models have become a hot research direction in the field of climate science due to their advantages in processing large-scale datasets. In this work, we propose a convolutional neural network-based (CNN) model capable of processing and analysing large-scale climate datasets from satellites, including multi-dimensional data including temperature, air pressure, humidity, and CO2 concentration. The input is historical climate data, and the spatial features are extracted through the convolutional layer, and then the feature fusion and final prediction output are performed through the fully connected layer. Finally, we utilized historical climate data as the training set and tested the model on data over multiple time periods. The results show that compared with traditional physical models, CNN-based models provide higher accuracy and lower prediction errors in predicting global average temperature changes, precipitation, and extreme weather events.