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Paper Type
ERF
Abstract
Dust storms pose significant challenges globally, impacting various aspects of human life, the environment, and infrastructure. This paper focuses on the predictive analysis of dust storms using GIS, artificial intelligence, and spatiotemporal data. Apart from their environmental consequences, dust storms also have profound health implications, including respiratory diseases and lung cancer. This paper aims to explore the MaxEnt modeling technique for dust storm forecasting, considering the complex interplay of natural and anthropogenic factors. By leveraging GIS, artificial intelligence, and spatiotemporal data, this research seeks to enhance our understanding of dust storm dynamics and improve prediction accuracy, thereby facilitating proactive mitigation strategies and risk management efforts.
Paper Number
1683
Recommended Citation
Bouzari, Soheil, "Dust Storm Predictive Analysis in Arizona: GIS, Artificial Intelligence, and Spatiotemporal Data" (2024). AMCIS 2024 Proceedings. 2.
https://aisel.aisnet.org/amcis2024/sig_green/sig_green/2
Dust Storm Predictive Analysis in Arizona: GIS, Artificial Intelligence, and Spatiotemporal Data
Dust storms pose significant challenges globally, impacting various aspects of human life, the environment, and infrastructure. This paper focuses on the predictive analysis of dust storms using GIS, artificial intelligence, and spatiotemporal data. Apart from their environmental consequences, dust storms also have profound health implications, including respiratory diseases and lung cancer. This paper aims to explore the MaxEnt modeling technique for dust storm forecasting, considering the complex interplay of natural and anthropogenic factors. By leveraging GIS, artificial intelligence, and spatiotemporal data, this research seeks to enhance our understanding of dust storm dynamics and improve prediction accuracy, thereby facilitating proactive mitigation strategies and risk management efforts.
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