Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

作     者：Wend Yam Serge Boris Ouedraogo Sibiri Tiemounou Moumouni Djibo Ali Doumounia Serge Roland Sanou Moumouni Sawadogo Idrissa Guira François Zougmore Wend Yam Serge Boris Ouedraogo;Sibiri Tiemounou;Moumouni Djibo;Ali Doumounia;Serge Roland Sanou;Moumouni Sawadogo;Idrissa Guira;François Zougmore

作者机构：Laboratoire Matériaux et Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Institut du Génie Informatique et Télécommunications Ecole Polytechnique de Ouagadougou Ouagadougou Burkina Faso Ecole Normale Supérieure Koudougou Burkina Faso Autorité de Régulation des Communications électroniques et des Postes Ouagadougou Burkina Faso Telecel Faso SA Ouagadougou Burkina Faso 

出 版 物：《Engineering（科研）》 (工程（英文）（1947-3931）)

年 卷 期：2022年第14卷第2期

页      面：85-93页

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

主　　题：Meteorological Visibility Commercial Microwave Link Machine Learning Climate Parameters Dust Attenuation 

摘      要：Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.