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
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
  • [1] Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning
    Li, Zhigang
    Shi, Haoze
    Yang, Xin
    Tang, Hong
    ATMOSPHERIC RESEARCH, 2022, 280
  • [2] Spatial and temporal characteristics of surface solar radiation in China and its influencing factors
    Jin, Hongmei
    Wang, Suichan
    Yan, Pengcheng
    Qiao, Liang
    Sun, Linhua
    Zhang, Ling
    FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
  • [3] Investigating Factors Influencing Deck Conditions of Concrete Bridge and Steel Bridge Using an Interpretable Machine Learning Framework
    Xiaoqiang Kong
    Zihao Li
    Jason Ryan Wallis
    Yunlong Zhang
    Data Science for Transportation, 2023, 5 (1):
  • [4] Nonlinear and spatially heterogeneous relationship between environmental factors and violent crime: Based on interpretable machine learning method
    Zhang, Yanji
    Zhu, Chunwu
    Dili Xuebao/Acta Geographica Sinica, 2024, 79 (08): : 2141 - 2156
  • [5] Variation in Surface Solar Radiation and the Influencing Factors in Xinjiang, Northwestern China
    Jin, Lili
    Li, Zhenjie
    He, Qing
    Abbas, Alim
    ADVANCES IN METEOROLOGY, 2022, 2022
  • [6] Relationship of winter wheat phenology with carbon and water flux and influencing factors in the North China Plain
    Wu, Jiujiang
    Wang, Nan
    Xing, Xuguang
    Ma, Xiaoyi
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 222
  • [7] Prediction of phytoplankton biomass and identification of key influencing factors using interpretable machine learning models
    Xu, Yi
    Zhang, Di
    Lin, Junqiang
    Peng, Qidong
    Lei, Xiaohui
    Jin, Tiantian
    Wang, Jia
    Yuan, Ruifang
    ECOLOGICAL INDICATORS, 2024, 158
  • [8] Exploring the key influencing factors of low-carbon innovation from urban characteristics in China using interpretable machine learning
    Wang, Wentao
    Li, Dezhi
    Zhou, Shenghua
    Wang, Yang
    Yu, Lugang
    ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, 2024, 107
  • [9] Spatial and temporal characteristics of surface solar radiation in China and its influencing factors (vol 10, 916748, 2022)
    Jin, Hongmei
    Wang, Suichan
    Yan, Pengcheng
    Qiao, Liang
    Sun, Linhua
    Zhang, Ling
    FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
  • [10] The features of solar radiation and surface radiation balance in north China plain: A case study in the Gucheng experimental station
    Liu, Jingmiao
    Ma, Jinyu
    Li, Shikui
    Liang, Hong
    Ren, Sanxue
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2009, 30 (05): : 577 - 585