A learning‐based approach to regression analysis for climate data–A case of Northeast China
Abstract Global climate change is an important issue that all of humanity needs to address together. Precipitation is an important climatic feature for agricultural development and food security, and the study of precipitation and its associated climatic factors is important for the analysis of glob...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.12797 |
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| author | Jiaxu Guo Yidan Xu Liang Hu Xianwei Wu Gaochao Xu Xilong Che |
| author_facet | Jiaxu Guo Yidan Xu Liang Hu Xianwei Wu Gaochao Xu Xilong Che |
| author_sort | Jiaxu Guo |
| collection | DOAJ |
| description | Abstract Global climate change is an important issue that all of humanity needs to address together. Precipitation is an important climatic feature for agricultural development and food security, and the study of precipitation and its associated climatic factors is important for the analysis of global change. As an important part of China's food production, Northeast China has a temperate monsoon climate with simultaneous rain and heat, which is favorable for crop growth. In this paper, a scientific workflow for climate data analysis with a learning‐based method is designed. Using climate data from typical models in CMIP6, a machine learning‐based approach is used to establish regression relationships between precipitation and climate variables such as temperature, humidity and wind speed in Northeast China, which is validated through a time series approach. We design a weight‐based model ensemble method and a learning‐based bias correction method, so that the ensemble model can achieve better performance. We also analyze the precipitation trends in Northeast China under the three Shared Socio‐economic Pathways (SSPs). This will help researchers to analyze the long‐term evolution and factors of climate. |
| format | Article |
| id | doaj-art-ab48e1eb19c74d1eb454a2198e76001d |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-ab48e1eb19c74d1eb454a2198e76001d2025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.12797A learning‐based approach to regression analysis for climate data–A case of Northeast ChinaJiaxu Guo0Yidan Xu1Liang Hu2Xianwei Wu3Gaochao Xu4Xilong Che5College of Computer Science and Technology Jilin University Changchun ChinaPostdoctoral Workstation, China Reinsurance (Group) Corporation Beijing ChinaCollege of Computer Science and Technology Jilin University Changchun ChinaCollege of Computer Science and Technology Jilin University Changchun ChinaCollege of Computer Science and Technology Jilin University Changchun ChinaCollege of Computer Science and Technology Jilin University Changchun ChinaAbstract Global climate change is an important issue that all of humanity needs to address together. Precipitation is an important climatic feature for agricultural development and food security, and the study of precipitation and its associated climatic factors is important for the analysis of global change. As an important part of China's food production, Northeast China has a temperate monsoon climate with simultaneous rain and heat, which is favorable for crop growth. In this paper, a scientific workflow for climate data analysis with a learning‐based method is designed. Using climate data from typical models in CMIP6, a machine learning‐based approach is used to establish regression relationships between precipitation and climate variables such as temperature, humidity and wind speed in Northeast China, which is validated through a time series approach. We design a weight‐based model ensemble method and a learning‐based bias correction method, so that the ensemble model can achieve better performance. We also analyze the precipitation trends in Northeast China under the three Shared Socio‐economic Pathways (SSPs). This will help researchers to analyze the long‐term evolution and factors of climate.https://doi.org/10.1002/eng2.12797climate datadata managementearth system modelmachine learningmodel ensembleprecipitation |
| spellingShingle | Jiaxu Guo Yidan Xu Liang Hu Xianwei Wu Gaochao Xu Xilong Che A learning‐based approach to regression analysis for climate data–A case of Northeast China Engineering Reports climate data data management earth system model machine learning model ensemble precipitation |
| title | A learning‐based approach to regression analysis for climate data–A case of Northeast China |
| title_full | A learning‐based approach to regression analysis for climate data–A case of Northeast China |
| title_fullStr | A learning‐based approach to regression analysis for climate data–A case of Northeast China |
| title_full_unstemmed | A learning‐based approach to regression analysis for climate data–A case of Northeast China |
| title_short | A learning‐based approach to regression analysis for climate data–A case of Northeast China |
| title_sort | learning based approach to regression analysis for climate data a case of northeast china |
| topic | climate data data management earth system model machine learning model ensemble precipitation |
| url | https://doi.org/10.1002/eng2.12797 |
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