New Insights into Global Warming: End-to-End Visual Analysis and Prediction of Temperature Variations

TL;DR

This paper introduces an end-to-end visual analysis and prediction framework for global temperature variations, utilizing classic models and deep learning to reveal climate change patterns with high accuracy.

Contribution

It presents a novel integrated approach combining visualization, prediction, and clustering using simple models tailored for global warming data, reducing complexity and enhancing interpretability.

Findings

Achieved high-accuracy long-term temperature forecasts with minimal error.

Revealed detailed spatiotemporal patterns of global warming.

Identified national-level temperature anomalies and emission patterns.

Abstract

Global warming presents an unprecedented challenge to our planet however comprehensive understanding remains hindered by geographical biases temporal limitations and lack of standardization in existing research. An end to end visual analysis of global warming using three distinct temperature datasets is presented. A baseline adjusted from the Paris Agreements one point five degrees Celsius benchmark based on data analysis is employed. A closed loop design from visualization to prediction and clustering is created using classic models tailored to the characteristics of the data. This approach reduces complexity and eliminates the need for advanced feature engineering. A lightweight convolutional neural network and long short term memory model specifically designed for global temperature change is proposed achieving exceptional accuracy in long term forecasting with a mean squared error of three times ten to the power of negative six and an R squared value of zero point nine nine nine nine. Dynamic time warping and KMeans clustering elucidate national level temperature anomalies and carbon emission patterns. This comprehensive method reveals intricate spatiotemporal characteristics of global temperature variations and provides warming trend attribution. The findings offer new insights into climate change dynamics demonstrating that simplicity and precision can coexist in environmental analysis.