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

摘要

The air temperature is a physical parameter that affects many fields of daily life, such as agriculture, energy and medical. Hence, the ability to accurately predict the air temperature is necessary to support the operational processes in those fields. Regarding the necessity, this study aims to develop prediction models to predict the air temperature in Padang city, West Sumatera. The models were developed by using two variants of artificial neural network, i.e. Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) and the hybrid of those models. The data set used in this study is monthly air temperature from January 2015 to December 2017 measured at Lembaga Ilmu Pengetahuan Indonesia (LIPI) weather measurement station in Muaro Anai, Padang. The CNN model was developed by considering several parameters, such as filter number and kernel size. Meanwhile, several parameters considered in MLP are hidden layers number and neuron number. Those parameters were selected by using a hyperparameter tuning scheme. By using the optimized parameter, we found that the CNN model produce the most satisfying results with the value of R2 is 0.9965. This indicated that the CNN model is the best model to be used to predict air temperature.