Deep learning-based method for weather forecasting: A case study in Itoshima
This is an incremental improvement for weather forecasting in a specific region, potentially aiding local applications.
The paper tackled weather forecasting in Itoshima, Japan, by introducing a multilayer perceptron model, which demonstrated superior performance compared to existing models like LSTM and RNN.
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.