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Frequent occurrences of extreme weather events substantially impact the livesof the less privileged in our societies, particularly in agriculture-inclinedeconomies. 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 oursocieties. However, research directions on how these technologies could helpreduce the impact of extreme weather are unclear. This makes it challenging toemploring digital technologies within the spheres of extreme weather. In thispaper, we employed the Delphi Best Worst method and Machine learning approachesto identify and assess the push factors of technology. The BWM evaluationrevealed that predictive nature was AI's most important criterion and role,while the mass-market potential was the less important criterion. Based on thisoutcome, we tested the predictive ability of machine elarning on a publilclyavailable dataset to affrm the predictive rols of AI. We presented themanagerial and methodological implications of the study, which are crucial forresearch and practice. The methodology utilized in this study could aiddecision-makers in devising strategies and interventions to safeguardsustainable production. This will also facilitate allocating scarce resourcesand investment in improving AI techniques to reduce the adverse impacts ofextreme events. Correspondingly, we put forward the limitations of this, whichnecessitate future research.

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