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Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence

Author

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  • Meng-Leong How

    (National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore)

  • Yong Jiet Chan

    (Faculty of Education, Monash University, Australia, 29 Ancora Imparo Way, Clayton VIC 3800, Australia)

  • Sin-Mei Cheah

    (Centre for Management Practice, Singapore Management University, 81 Victoria Street, Singapore 188065, Singapore)

Abstract

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, food availability, food quality and safety, and the resilience of natural resources. Exemplars are provided in this paper to illustrate how computational simulations can be used to produce forecasts of good and bad conditions in food security using multi-variant optimizations. The forecast of these future scenarios is useful for informing policy makers and stakeholders across domain verticals, so they can make decisions that are favorable to global food security.

Suggested Citation

  • Meng-Leong How & Yong Jiet Chan & Sin-Mei Cheah, 2020. "Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence," Sustainability, MDPI, vol. 12(15), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6272-:d:394274
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    References listed on IDEAS

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    1. Anna Sperotto & Josè Luis Molina & Silvia Torresan & Andrea Critto & Manuel Pulido-Velazquez & Antonio Marcomini, 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks," Sustainability, MDPI, vol. 11(17), pages 1-34, August.
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    Cited by:

    1. Ali, Mohd Helmi & Chung, Leanne & Kumar, Ajay & Zailani, Suhaiza & Tan, Kim Hua, 2021. "A sustainable Blockchain framework for the halal food supply chain: Lessons from Malaysia," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    2. Julia Kleineidam, 2022. "Distinguishing Organisational Profiles of Food Loss Management in Logistics," Logistics, MDPI, vol. 6(3), pages 1-23, August.

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