Article Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet- Bulb Depression Qi Luo 1, *, Manouchehr Shokri 2 , Adrienn Dineva 2, * 1 School of continuing education, Chengdu Normal University, Chengdu, Sichuan Province, 611130, China; 2 Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany; manouchehr.shokri@uni-weimar.de *Corresponding authors email: a.dineva@ieee.org Abstract: The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, who dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures. Keywords: wet-bulb depression; relative humidity; ANFIS; artificial neural network; LSSVM 1. Introduction The main factors to control the quantity of moist air are the temperature and pressure. As the air temperature increases, the amount of water vapor is also increasing [1,2]. The widely applied parameter in practice for determining a characteristic of air is the dry-bulb temperature, which is known as the air temperature. Since the obtained air temperature by a thermometer is not function of the air humidity, it is named as dry bulb [3,4]. Conversely, the wet-bulb temperature obtained by a wet thermometer which is affected by the airflow. The rate of evaporative cooling that is a type of cooling with the capability of removing the moisture from a surface usually is measured by a thermometer. The main distinction of the wet bulb and dry bulb temperatures is their amount. Except at relative humidity equal to 1, all the time the quantity of the dry bulb temperature is more than temperature of wet bulb [5,6]. According to guidelines, the difference between the wet bulb and dry bulb temperatures is known as the wet-bulb depression [3,5]. The wet-bulb depression is an important term to determine the relative humidity and this will be shown at following. The quantity of water vapor in the air in comparison with the full saturation is known as relative humidity. In other words, relative humidity can be defined as the ratio of the amount of water vapor in the air at Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 6 February 2020 doi:10.20944/preprints202002.0075.v1 © 2020 by the author(s). Distributed under a Creative Commons CC BY license.