Analysis and Evaluation of Global Temperature Changes and the Prediction of Future Global Temperature

Abstract

Temperatures have continued to rise in recent years, many areas have shown extreme temperatures, and global warming has been a perennial topic. We use various optimization prediction and optimization evaluation methods in this paper to determine the trends and causes of global temperature variation and make effective suggestions for them. We first constructed a first-order differential temperature model and found that the temperature rise in March 2022 was larger than that observed in the past 10 years. By constructing a prediction model (BP) based on BP neural network and a grey temperature prediction model (GAGM) optimized based on adaptive genetic algorithm, and by importing the data, we find that the average temperature in 2116 and 2145 will reach 20℃. In addition, if the correlation factor evaluation model (FAEM) is constructed in the country, and we conclude that there is a correlation between location, time and temperature. Next, we analyzed key environmental factors, such as volcanic eruptions, forest fires, and COVID-19. We imported the CO 2 concentration and temperature data into the FAEM evaluation model to obtain the results that natural disasters can lead to the temperature increase. Then nine environmental factors, including COND, NYKC, NYXH, GDP, PJRK, LJL, LZMJ, KJSP, and GJJY. Taking these factors as independent variables and temperature as the dependent variables, COND, NYXH, PJRK, LJMJ, JSL are the main causes of global temperature change and KJSP. This study provides an assessment of future global temperature changes, making recommendations for policymakers to optimize the energy mix and plant trees to effectively mitigate the global temperature rise.