A NEUROMORPHIC ANALYSIS OF CLIMATE PATTERNS FOR COMPLEX ENVIRONMENTAL FRACTAL MODELING

ALZU’BI Shadi, Tarek Kanan (), Mohammed Elbes () and Muder Almiani ()
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ALZU’BI Shadi: Alzaytoonah University of Jordan, Amman, Jordan
Tarek Kanan: Alzaytoonah University of Jordan, Amman, Jordan
Mohammed Elbes: Alzaytoonah University of Jordan, Amman, Jordan
Muder Almiani: Gulf University for Science and Technology, Kuwait City, Kuwait

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-14

Abstract: This study investigates the use of neuromorphic computing, particularly spiking neural networks (SNNs) and advanced neuromorphic hardware, to model and forecast climate patterns. Our neuromorphic system achieves high prediction accuracy, maintaining a Mean Squared Error (MSE) as low as 0.08, even with increasing data volumes. The system operates with notable energy efficiency, consuming just 0.15 J per inference at higher data loads. This efficiency, coupled with a throughput of 800 inferences per second, underscores the system’s capability to handle large-scale data effectively.The neuromorphic approach addresses key challenges in scalability and energy consumption, presenting a robust solution for real-time climate data analysis. By continuously adapting to new data inputs, the system ensures accurate and timely predictions, essential for applications in environmental monitoring and decision-making. The integration of artificial intelligence algorithms with neuromorphic architectures not only reduces computational costs but also enhances the interpretability of complex climate dynamics.These findings highlight the transformative potential of brain-like computing in environmental modeling, offering a scalable, efficient, and adaptable tool for climate prediction and analysis.

Keywords: Neuromorphic Computing; Fractal Climate Pattern Analysis; Environmental Modeling; Spiking Neural Networks; Brain-Like Computing; Predictive Modeling; Energy-Efficient Algorithms; Fractal Geometry; Scaling Behavior (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25401048

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