Implementation of Convolutional Neural Network and Multilayer Perceptron in Predicting Air Temperature in Padang

Isman Kurniawan, Lusi Sofiana Silaban, Devi Munandar

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · 26 December 2020

In one sentence. Compares CNN, MLP and hybrid CNN-MLP for air temperature prediction at a Padang station; CNN gives the highest R2.

Abstract

Weather prediction is usually performed for a reference in planning future activity. The prediction is performed by considering several parameters, such as temperature, air pressure, humidity, wind, rainfall, and others. In this study, the temperature, as one of weather parameters, is predicted by using time series from January 2015 to December 2017. The data was obtained from Lembaga Ilmu Pengetahuan Indonesia (LIPI) weather measurement station in Muaro Anai, Padang. The predictions were carried out by using Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), and the hybrid of CNN-MLP methods. The parameters used in the CNN method, such as the number of filters and kernel size, and used in the MLP method, such as the number of hidden layers and number of neurons, were selected by performing the hyperparameter tuning procedure. After obtaining the best parameters for both methods, the performance of both methods was evaluated by calculating the value of Root Mean Square Error (RMSE) and R2. Based on the results, we found that the prediction by CNN is more accurate than other method. This is indicated by the highest value of R2 of the prediction obtained by CNN method.

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