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Talk · ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems · January 28, 2024

Meteorology Predicting Technology Based on Artificial Intelligence Algorithms

Gulnara Abitova, Daniyar Mukashev, Elvira Aitmukhanbetova, Mira Rakhimzhanova, Gulzhan Uskenbayeva, Korlan Kulniyazova

Elvira Aitmukhanbetova
Read on dx.doi.orgDOI 10.1109/icetsis61505.2024.10459407

Abstract

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage.

Meteorological Phenomena and SimulationsArtificial Intelligence ApplicationsHydrological Forecasting Using AIweather forecastingdeep learningartificial neural networks

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