Using Python to Predict Global City Temperatures for 400+ Cities

作     者：Rishi Manjure Rishi Manjure

作者机构：California Institute of Technology Pasadena USA 

出 版 物：《Atmospheric and Climate Sciences》 (大气和气候科学（英文）)

年 卷 期：2023年第13卷第4期

页      面：607-615页

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

主　　题：Machine Learning Climate Change Sustainability Python Atmospheric Sciences Modeling 

摘      要：The purpose of this investigation was to use Python to model global city temperatures for 400+ cities for many decades. The process used a compilation of secondary data to find my renowned sources and use different regression models to plot temperatures. Climate change is an impending crisis for our Earth, and modeling its changes using Machine Learning will be crucial to understanding the next steps to combat it. With this model, researchers can understand which area is most harshly affected by climate change leading to prioritization and solutions. They can also figure out the next sustainable solutions based on climate needs. By using KNeighbors and other regressors, we can see an increase in temperature worldwide. Although there is some error, which is inevitable, this is mitigated through several measures. This paper provides a simple yet critical understanding of how our global temperatures will increase, based on the last 200+ years.