Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{168462,
        author = {Shivam Kumar and Adarsha Kumar},
        title = {Deep Learning Under the Clouds: Weather Forecasting Using Keras Model with SimpleRNN for Precision Metrological Insights},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1158-1164},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168462},
        abstract = {Weather forecasting is the software of technology and era to expect the country of the environment for a given region. Ancient climate forecasting techniques usually depend on determined patterns of occasions, additionally termed pattern recognition. Agriculture, transportations, and energy are sectors that depend on heigh resolution weather models, which typically consumes many hours of large Heigh Performance Computing (HPC) systems to deliver timely results. Many users cannot afford to run the desired resolution and are forced to use low resolution output. In this paper we proposed to evaluate a strategy based on a deep learning neural network to learn a heigh-resolution representation from low- resolution predictions using weather forecasting as a practical use case. We take deep learning approach using SimpleRNN method to obtain our output. The device will expect weather based on parameters with temperature, pressor and humidiy.},
        keywords = {Deep Learning, Recurrent Neural Network, Adam optimizer, Keras Model, SimpleRNN, RMSprop Optimizer.},
        month = {October},
        }