Building a temperature forecasting model for the city with the regression neural network (RNN)

TL;DR

This paper explores the application of recurrent neural networks to improve urban temperature forecasting, addressing challenges posed by complex weather patterns and data limitations, with a focus on Vietnam.

Contribution

It introduces a novel RNN-based model tailored for city temperature prediction, enhancing accuracy over traditional methods.

Findings

RNN model outperforms baseline models in temperature prediction accuracy

The approach effectively captures complex weather patterns

The model demonstrates robustness with limited data

Abstract

In recent years, a study by environmental organizations in the world and Vietnam shows that weather change is quite complex. global warming has become a serious problem in the modern world, which is a concern for scientists. last century, it was difficult to forecast the weather due to missing weather monitoring stations and technological limitations. this made it hard to collect data for building predictive models to make accurate simulations. in Vietnam, research on weather forecast models is a recent development, having only begun around 2000. along with advancements in computer science, mathematical models are being built and applied with machine learning techniques to create more accurate and reliable predictive models. this article will summarize the research and solutions for applying recurrent neural networks to forecast urban temperatures.