Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence
| Autor: | Sin Mei Cheah, Yong Jiet Chan, Meng Leong How |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Counterfactual thinking
Computer science Geography Planning and Development TJ807-830 010501 environmental sciences Management Monitoring Policy and Law TD194-195 Bayesian 01 natural sciences Renewable energy sources cognitive scaffolding counterfactual GE1-350 Resilience (network) resilience 0105 earth and related environmental sciences global food security index Food security Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry 05 social sciences Probabilistic logic 050301 education artificial intelligence sustainability Natural resource Environmental sciences machine learning Scale (social sciences) Sustainability Food systems Artificial intelligence Food quality business 0503 education predictive modeling AI for social good |
| Zdroj: | Sustainability, Vol 12, Iss 6272, p 6272 (2020) Sustainability Volume 12 Issue 15 |
| ISSN: | 2071-1050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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