Using interpretable boosting algorithms for modeling environmental and agricultural data

We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile...

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Veröffentlicht in:Scientific reports Jg. 13; H. 1; S. 12767 - 16
Hauptverfasser: Obster, Fabian, Heumann, Christian, Bohle, Heidi, Pechan, Paul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 07.08.2023
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Agricultural economics
Agriculture
Algorithms
Altitude
Climate change
Crop damage
Drought
Environmental hazards
Farmers
Farms
Generalized linear models
Human subjects
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Sparsity
Variables
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile and Tunisia against climate hazards. We show how group structures can be considered and how interactions can be found in high-dimensional datasets using a novel 2-step boosting approach. The advantages and efficacy of the proposed method are shown and discussed. Results indicate that the presence of interaction effects only improves predictive power when included in two-step boosting. The most important variable in predicting all types of vulnerabilities are natural assets. Other important variables are the type of irrigation, economic assets and the presence of crop damage of near farms.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-39918-5