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Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction

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  • Meyer, Hanna
  • Reudenbach, Christoph
  • Wöllauer, Stephan
  • Nauss, Thomas

Abstract

Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions.

Suggested Citation

  • Meyer, Hanna & Reudenbach, Christoph & Wöllauer, Stephan & Nauss, Thomas, 2019. "Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction," Ecological Modelling, Elsevier, vol. 411(C).
    • Handle: RePEc:eee:ecomod:v:411:y:2019:i:c:s0304380019303230
    DOI: 10.1016/j.ecolmodel.2019.108815
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    References listed on IDEAS

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    1. Antoine Stevens & Marco Nocita & Gergely Tóth & Luca Montanarella & Bas van Wesemael, 2013. "Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-13, June.
    2. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
    3. Eric S Walsh & Betty J Kreakie & Mark G Cantwell & Diane Nacci, 2017. "A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
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    Cited by:

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    2. Jiang, Xiaoman & Wang, Yuntao & A., Yinglan & Wang, Guoqiang & Zhang, Xiaojing & Ma, Guangwen & Duan, Limin & Liu, Kai, 2024. "Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques," Agricultural Water Management, Elsevier, vol. 295(C).
    3. Wadoux, Alexandre M.J.-C. & Heuvelink, Gerard B.M. & de Bruin, Sytze & Brus, Dick J., 2021. "Spatial cross-validation is not the right way to evaluate map accuracy," Ecological Modelling, Elsevier, vol. 457(C).
    4. Gustavo Larrea‐Gallegos & Ian Vázquez‐Rowe, 2022. "Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 225-239, February.
    5. Metz-Peeters, Maike, 2023. "The Effects of Mandatory Speed Limits on Crash Frequency - A Causal Machine Learning Approach," Ruhr Economic Papers 982, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2023.

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