Deep learning-based method for weather forecasting: A case study in Itoshima

TL;DR

A multilayer perceptron model tailored for weather forecasting in Itoshima, Kyushu, Japan is introduced, demonstrating superior performance compared to existing models, surpassing benchmarks such as Long Short-Term Memory and Recurrent Neural Networks.

Abstract

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.

Figures (48)

• Figure 1: Location of Ito campus, Kyushu University
• Figure 2: Temperature: histogram
• Figure 3: Humidity: histogram
• Figure 4: Wind speed: histogram
• Figure 5: Wind direction: histogram