Continuous-Time Deep Learning for Climate Forecasting: Neural ODEs Applied to Benghazi Temperature Data Raja Mohammad Elbarjo
DOI:
https://doi.org/10.65405/.v10i37.396الكلمات المفتاحية:
Neural Ordinary Differential Equations, Weather Prediction, Deep Learning, Benghazi, Neural Networks, Temperatureالملخص
This work discusses using Neural Ordinary Differential Equations (ODEs) for
forecasting temperature values. We discuss the impact of learning rate reduction on performance
in the model, and display impressive gains in training loss and test loss after the learning rate was
reduced from 0.075 to 0.001. This study highlights the importance of learning rate tuning for
better model generalization and more accurate predictions. We also provide a detailed description
of the data preprocessing steps, the model architecture, and the results of the experiments,
including comparisons of the performance before and after the adjustment.
التنزيلات
المراجع
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Continuous-Time Deep Learning for Climate Forecasting -------------------- Raja Mohammad Elbarjo et. al
مـجلـة الـعـلـوم الشـامـلـة المجلد )10(، العدد )37(، )نوفمبر2025( ردمد: 3014-6266 :ISSN 1-968
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الرخصة
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