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Validation of scenario generation for decision-making using machine learning prediction models

A case study for crop yield

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Abstract

Machine learning provides valuable information for data-driven decision-making. However, real-world problems commonly include uncertainties and the features needed to generate the prediction outputs are random variables. Even the most reliable machine learning models may not be helpful for decision-makers when the decisions must be taken before the values of features used in machine learning models are realized. To support decision-making under uncertainty, we propose a scenario generation procedure for stochastic programs that incorporates the uncertainties in both prediction features and the machine learning model prediction error. A statistical test is implemented to assess the reliability of the scenario sets by comparison with corresponding historical observations. We test the whole procedure in a case study for crop yield in Midwest.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grants 1828942, 1929681, and 1842097, project 1011702 from the USDA National Institute of Food and Agriculture, The Foundation for Food and Agriculture Research, and Plant Sciences Institute at Iowa State University.

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Authors and Affiliations

  1. Iowa State University, Ames, USA

    Görkem Emirhüseyinoğlu, Mohsen Shahhosseini, Gui** Hu & Sarah M. Ryan

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  1. Görkem Emirhüseyinoğlu
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  2. Mohsen Shahhosseini
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  3. Gui** Hu
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  4. Sarah M. Ryan
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Correspondence to Görkem Emirhüseyinoğlu.

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Emirhüseyinoğlu, G., Shahhosseini, M., Hu, G. et al. Validation of scenario generation for decision-making using machine learning prediction models. Optim Lett (2023). https://doi.org/10.1007/s11590-023-02023-7

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