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Comparison between two deep learning models for temperature pbkp_rediction at guayaquil

Abstract:

Weather pbkp_rediction is a subject that is constantly changing everywhere in the world because of the different methods that are applied. This study is done in the city of Guayaquil remembering that weather forecast has played a very important role for many people who belong to different fields of research because it needs to have a minimum margin of error in order to meet the different objectives of each researcher. This paper aims to find the best type of MLP or LSTM neural network model that has a lower margin of error when pbkp_redicting the weather at a specific weather station in the aforementioned city. In order to assess the accuracy between these pbkp_rediction models, the Euclidean estimation standard was used. With the results of this comparison, it is hoped to contribute to the pbkp_rediction of the climate to be able to help not only the researchers but also the farmers, tourists, and people in general whose work depends on this topic.