Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning

被引：0
|
作者：
Li, Zhigang [1 ]
Shi, Haoze [1 ]
Yang, Xin [1 ]
Tang, Hong [2 ]
机构：
[1] College of Global Change and Earth System Science, Beijing Normal University, Beijing,100875, China
[2] State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing,100875, China
来源：
Atmospheric Research | 2022年 / 280卷
关键词：
Machine learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [41] Investigating the relationship between urban sprawl and urban heat island using remote sensing and machine learning approaches
    Rana, Biswarup
    Bandyopadhyay, Jatisankar
    Halder, Bijay
    THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (05) : 4139 - 4160
  • [42] Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning
    Mehrafarin Ramezani
    Pauline Mouches
    Eunjin Yoon
    Deepthi Rajashekar
    Jennifer A. Ruskey
    Etienne Leveille
    Kristina Martens
    Mekale Kibreab
    Tracy Hammer
    Iris Kathol
    Nadia Maarouf
    Justyna Sarna
    Davide Martino
    Gerald Pfeffer
    Ziv Gan-Or
    Nils D. Forkert
    Oury Monchi
    Scientific Reports, 11
  • [43] Investigating the relationship between urban sprawl and urban heat island using remote sensing and machine learning approaches
    Biswarup Rana
    Jatisankar Bandyopadhyay
    Bijay Halder
    Theoretical and Applied Climatology, 2024, 155 : 4161 - 4188
  • [44] Identification of factors influencing net primary productivity of terrestrial ecosystems based on interpretable machine learning --evidence from the county-level administrative districts in China
    Yi, Zhaoqiang
    Wu, Lihua
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2023, 326
  • [45] Analysis of the influencing factors of the dust on the surface of photovoltaic panels and its weakening law to solar radiation d A case study of Tianjin
    Yao, Wanxiang
    Han, Xiao
    Huang, Yu
    Zheng, Zhimiao
    Wang, Yan
    Wang, Xiao
    ENERGY, 2022, 256
  • [46] Identifying the combined impact of human activities and natural factors on China's avian species richness using interpretable machine learning methods
    Liu, Qi
    Wu, Zeqiang
    Feng, Shaoyan
    Li, Mingjia
    Deng, Ligang
    Fan, Yifan
    Qian, Xin
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2025, 376
  • [47] Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application
    Wang, Ying
    Shi, Wenjuan
    Wen, Tianyang
    AGRICULTURAL WATER MANAGEMENT, 2023, 277
  • [48] Identifying factors influencing energy saving cognition-behavior gaps in shared residential spaces using interpretable machine learning: A dormitory case study
    Wang, Hanqi
    Li, Jie
    Mao, Peng
    JOURNAL OF BUILDING ENGINEERING, 2025, 100
  • [49] Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China
    Wang, Xiaoxi
    Zhang, Yaojun
    Yu, Danlin
    REMOTE SENSING, 2023, 15 (07)
  • [50] Identifying Factors Influencing Surface Deformations from Underground Mining Using SAR Data, Machine Learning, and the SHAP Method
    Cieslik, Konrad
    Milczarek, Wojciech
    Warchala, Ewa
    Kosydor, Pawel
    Rozek, Robert
    REMOTE SENSING, 2024, 16 (13)
12345