Author(s)

Wend Yam Serge Boris Ouedraogo¹, Sibiri Tiemounou², Moumouni Djibo¹, Ali Doumounia^1,3, Serge Roland Sanou^1,4, Moumouni Sawadogo¹, Idrissa Guira⁵, François Zougmore¹

¹Laboratoire Matériaux et Environnement, Université Joseph KI-ZERBO, Ouagadougou, Burkina Faso.
²Institut du Génie Informatique et Télécommunications, Ecole Polytechnique de Ouagadougou, Ouagadougou, Burkina Faso.
³Ecole Normale Supérieure, Koudougou, Burkina Faso.
⁴Autorité de Régulation des Communications électroniques et des Postes, Ouagadougou, Burkina Faso.
⁵Telecel Faso SA, Ouagadougou, Burkina Faso.

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.

Meteorological Visibility, Commercial Microwave Link, Machine Learning, Climate Parameters, Dust, Attenuation

Ouedraogo, W. , Tiemounou, S. , Djibo, M. , Doumounia, A. , Sanou, S. , Sawadogo, M. , Guira, I. and Zougmore, F. (2022) Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition. Engineering, 14, 85-93. doi: 10.4236/eng.2022.142008.