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

arXiv: https://arxiv.org/abs/2403.14918 · HTML: https://mlsanity.com/paper/2403.14918/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)

## Built on by (most cited first, 2 of 2)

- [Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation](https://mlsanity.com/paper/2508.10938/) (2025)
- [BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations](https://mlsanity.com/paper/2605.12534/) (2026)

## Abstract (arXiv)

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.