Utilizing time series data from 1961 to 2019 recorded around the world and machine learning to create a Global Temperature Change Prediction Model

Abstract

Since 1880, the Earth's temperature has increased at a pace of 0.14° Fahrenheit (0.08° Celsius) every decade; however, the rate of warming since 1981 is more than double that, at 0.32 °F (0.18 °C) per decade. Based on NOAA's temperature data, 2021 was the sixth hottest year on record. Warmer temperatures can also lead to a chain reaction of other global changes. That's because increasing air temperature affects the oceans, weather patterns, snow and ice, and plants and animals. The warmer it gets, the more severe the effects on people and the environment. The global average surface temperature has risen at an average rate of 0.17 °F per decade since 1901.The Global Surface Temperature Change data recorded by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS) was used in this paper.This article attempted to design a model for Time Series Data from 1961 to 2020 for Global Temperature Change Prediction with the help of machine learning algorithms. Therefore, Extra Trees, light gradient boosting machine, Random Forest, K nearest neighbors, gradient boosting, and Bayesian ridge algorithms were investigated to create a Global Temperature Change Prediction Model and the evaluation criteria such as MAE (C°), MSE (C°), RMSE (C°), R2 and RMSLE (C °) and MAPE (C °) were calculated, and also the execution time of the algorithms was obtained in seconds. The obtained results showed that the Extra Trees algorithm has the best performance in predicting Global Temperature Change.

Keywords

• The rate of warming
• Temperature change
• NASA-GISS
• Time series data
• Machine learning