Artificial Intelligence and Innovation to Reduce the Impact of Extreme Weather Events on Sustainable Production

• URL: http://arxiv.org/abs/2210.08962v1
• Date: Wed, 21 Sep 2022 06:52:39 GMT
• Title: Artificial Intelligence and Innovation to Reduce the Impact of Extreme Weather Events on Sustainable Production
• Authors: Derrick Effah, Chunguang Bai, and Matthew Quayson
• Abstract summary: unpredictability of extreme weather endangers sustainable production and life on land. Modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear.
• Score: 1.290382979353427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Frequent occurrences of extreme weather events substantially impact the lives of the less privileged in our societies, particularly in agriculture-inclined economies. The unpredictability of extreme fires, floods, drought, cyclones, and others endangers sustainable production and life on land (SDG goal 15), which translates into food insecurity and poorer populations. Fortunately, modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather in our societies. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear. This makes it challenging to emploring digital technologies within the spheres of extreme weather. In this paper, we employed the Delphi Best Worst method and Machine learning approaches to identify and assess the push factors of technology. The BWM evaluation revealed that predictive nature was AI's most important criterion and role, while the mass-market potential was the less important criterion. Based on this outcome, we tested the predictive ability of machine elarning on a publilcly available dataset to affrm the predictive rols of AI. We presented the managerial and methodological implications of the study, which are crucial for research and practice. The methodology utilized in this study could aid decision-makers in devising strategies and interventions to safeguard sustainable production. This will also facilitate allocating scarce resources and investment in improving AI techniques to reduce the adverse impacts of extreme events. Correspondingly, we put forward the limitations of this, which necessitate future research.

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