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Outdoor Relative Humidity Prediction via Machine Learning Techniques

R. T. Zarinkamar1, and R. V. Mayorga1 *

  1. Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2 Canada

*Corresponding author. Tel.: 306-585-4726; fax: 306-585-4855. E-mail address: (R. V. Mayorga).


In an environmental control system, relative humidity (RH) is a very important factor because of its direct impact on humans or even animals and plants. However, there are few studies focused on prediction of humidity variables. The main objective of this paper is to show the capability of machine learning algorithms for RH prediction. In this study, a Long-Short Term Memory (LSTM) and four popular machine learning algorithms; namely, Multi-Layer Perceptron (MLP), Random Forest (RF), k-Nearest Neighbor (KNN) and Support Vector Machine for Regression (SVMR) are presented for a year period of time-series relative humidity to predict for a particular case in an Italian city. In order to have precise performance, data pre-processing is done before running the models. This thorough examination proves the positive effect of all Machine Learning-based algorithms in time-series relative humidity prediction based on predictive accuracy. Over the different metrics, LSTM indicates the best performance among all considered algorithms.

Keywords: deep learning, LSTM, machine learning, relative humidity, weather prediction

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