Article Details

Abstract

The uncertainty and complexity of global climate change have put forward higher requirements for the accuracy of weather forecasting, and this research aims to develop an efficient weather forecasting model based on Machine Learning to cope with the challenges brought by global climate change. In this study, Python is used to integrate statistical modeling techniques such as regression analysis and Decision Tree algorithm to analyze and process meteorological data in depth. The results show that the Decision Tree model has unique advantages in processing nonlinear meteorological data, and its forecasting accuracy and generalization ability are better than those of traditional regression models. The Linear Regression analysis model is robust in capturing the linear relationship of data. The model shows good generalization ability on different datasets and can meet the performance requirements of real-time forecasting. The results of this research provide new perspectives and tools for the field of weather forecasting, and lay a foundation for the future application of Machine Learning in meteorology.

Keywords

Machine Learning; weather forecasting model; Decision Tree; Linear Regression

References

1. [1] Fu Xiaobiao, Hou Jiaqi, Li Baoju, etc A two mode weather classification method and its application in photovoltaic power probability prediction [J]. Power Generation Technology, 2024,45 (2): 299-311
2. [2] White as snow Research on Short term Photovoltaic Power Prediction Method Based on Improved Similarity Days and LSTM Neural Network [D]. Jinan: Shandong University, 2023
3. [3] Xu Minjie Network Credit Default Prediction Based on Ensemble Learning [D]. Chongqing: Southwest University, 2023
4. [4] Yang Yingxin Prediction and Research of Multi source Meteorological Observation Data Based on Deep Learning [D]. Nanjing: Nanjing University of Information Science and Technology, 2022
5. [5] Shi Pengfei Research on the Detection Method and Spatial Distribution of Oil Pollution in Oilfield Soil under Multi source Remote Sensing Data Collaboration [D]. Changchun: Jilin University, 2023
6. [6] Wang Qingjie Research on Runoff Prediction Machine Learning Model Based on Feature Selection and Time Frequency Analysis [D]. Urumqi: Xinjiang Agricultural University, 2022
7. [7] Song Ting Intelligent Retrieval Method for Library Literature Resources Based on Decision Tree [J]. Automation Technology and Application, 2024, 43 (4): 63-66
8. [8] Peng Yuxin, Zhu Zhixu, Xiao Dunming, etc Economic evaluation of large-scale contrast agent CT enhanced scanning based on decision tree model [J]. World Clinical Drugs, 2024, 45 (5): 497-505
9. [9] Wei Zehui Research on Economic Model Prediction of Photovoltaic Power Generation System Based on Multiple Nonlinear Regression Analysis [J]. Power Plant Systems Engineering, 2024, 40 (3): 1-5
10. [10] He Tiantian Research on Weather Prediction Method Based on Spatiotemporal Self Supervision [D]. Jinan: Shandong University, 2023

Funding

2024 Shandong University of Technology College Students Innovation and Entrepreneurship Training Program

About the authors

Guan Donghe (2004-), male, Han ethnicity, from Weifang, Shandong Province, undergraduate studies, research direction: big data management and application; Su Bo (2004-), male, Han ethnicity, from Heze, Shandong, is currently pursuing a bachelor's degree with a research focus on electronic supply chain.