Accurate weather forecasting is of paramount importance for a wide range of practical applications, drawing substantial scientific and societal interest. However, the intricacies of weather systems pose substantial challenges to accurate predictions. This research introduces a multilayer perceptron model tailored for weather forecasting in Itoshima, Kyushu, Japan. Our meticulously designed architecture demonstrates superior performance compared to existing models, surpassing benchmarks such as Long Short-Term Memory and Recurrent Neural Networks.
@article{arxiv.2403.14918,
title = {Deep learning-based method for weather forecasting: A case study in Itoshima},
author = {Yuzhong Cheng and Linh Thi Hoai Nguyen and Akinori Ozaki and Ton Viet Ta},
journal= {arXiv preprint arXiv:2403.14918},
year = {2024}
}