Using the risk of spatial extrapolation by machine-learning models to assess the reliability of model predictions for conservation

被引:0
|
作者
Kevin J. Gutzwiller
Kimberly M. Serno
机构
[1] Baylor University,Department of Biology
[2] Baylor University,Institute for Ecological, Earth, and Environmental Sciences
来源
Landscape Ecology | 2023年 / 38卷
关键词
Boosted regression trees; Importance values; Multiscale spatial optimization; Predictive models; Random forests; Training space;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:1363 / 1372
页数:9
相关论文
共 50 条
  • [21] Using machine-learning to create predictive material property models
    Wolverton, Chris
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2016, 252
  • [22] Using machine-learning to create predictive material property models
    Wolverton, Chris
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 255
  • [23] Consumer credit-risk models via machine-learning algorithms
    Khandani, Amir E.
    Kim, Adlar J.
    Lo, Andrew W.
    JOURNAL OF BANKING & FINANCE, 2010, 34 (11) : 2767 - 2787
  • [24] Reliability Assessment of Machine Learning Models in Hydrological Predictions Through Metamorphic Testing
    Yang, Yang
    Chui, Ting Fong May
    WATER RESOURCES RESEARCH, 2021, 57 (09)
  • [25] Geographical Gaussian Process Regression: A Spatial Machine-Learning Model Based on Spatial Similarity
    Jiao, Zhenzhi
    Tao, Ran
    GEOGRAPHICAL ANALYSIS, 2025,
  • [26] Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions
    Lai, Dade
    Demartino, Cristoforo
    Xiao, Yan
    ENGINEERING STRUCTURES, 2023, 281
  • [27] Spatial mapping Zataria multiflora using different machine-learning algorithms
    Edalat, Mohsen
    Dastres, Emran
    Jahangiri, Enayat
    Moayedi, Gholamreza
    Zamani, Afshin
    Pourghasemi, Hamid Reza
    Tiefenbacher, John P.
    CATENA, 2022, 212
  • [28] Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
    Hatfield, Daniel
    Hasager, Charlotte Bay
    Karagali, Ioanna
    WIND ENERGY SCIENCE, 2023, 8 (04) : 621 - 637
  • [29] Credit-Risk Prediction Model Using Hybrid Deep - Machine-Learning Based Algorithms
    Melese, Tamiru
    Berhane, Tesfahun
    Mohammed, Abdu
    Walelgn, Assaye
    Scientific Programming, 2023, 2023
  • [30] Predicting geogenic groundwater arsenic contamination risk in floodplains using interpretable machine-learning model
    Fan, Ruiyu
    Deng, Yamin
    Du, Yao
    Xie, Xianjun
    ENVIRONMENTAL POLLUTION, 2024, 340