Policy product: Using machine learning to mitigate climate disasters
Climate change induced by human activities is a threat to us all, with consequences for various aspects of life (high temperatures, droughts, fires, floods) and worsening weather conditions. Faced with this situation, it is important to set up early warning systems. These systems, which aim to warn...
Kaydedildi:
| Yayımlandı: | URI:https://journals.openedition.org/ctd, |
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| Yazar: | |
| Materyal Türü: | Article ou chapitre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
Communication, technologies et développement
2024
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| Konular: | |
| Online Erişim: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Özet: | Climate change induced by human activities is a threat to us all, with consequences for various aspects of life (high temperatures, droughts, fires, floods) and worsening weather conditions. Faced with this situation, it is important to set up early warning systems. These systems, which aim to warn people of impending storms, floods or droughts, are not insignificant. They are an effective tool for saving lives and reducing economic losses and damage caused by meteorological, hydrological and climatic hazards1 . With this in mind, United Nations Secretary-General António Guterres launched a global appeal in 2022 for all the world’s inhabitants to be protected by a multi-hazard early warning system (MHEWS) by 2027. Defined as the set of algorithms used to analyse data in order to identify patterns, trends, correlations and other relevant information that improve the decision-making process, Artificial Intelligence (AI) is increasingly used in weather prediction. This article therefore analyses its various implications for risk and disaster prevention, through its contributions to early warning systems. |
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