Several factors can attenuate radio signal between transmitting and receiving antenna. One can cite: vegetation, atmospheric gases, fog, water vapor, transmission instruments, rain, temperature, etc... The sources of attenuation differ according to the climate and the relief of each continent or even each country. In this work we aim to show that there is link between microwave signal attenuation and weather visibility in the presence of dust. Weather visibility is a very important factor for the safety of road, sea, rail and air transportation. In the presence of dust, the visibility is strongly reduced and there is also a strong attenuation of the microwave signal propagating between two antennas. By performing a linear regression on the attenuation-visibility scatter plot, we propose a method for real-time estimation of the visibility knowing the microwave signals attenuation. A correlation measurement between the visibility estimated by our method from the real attenuation data of the mobile phone operator Telecel Faso SA (Burkina Faso) and the visibility measured by the National Meteorological Agency of Burkina Faso (ANAM) gave a correlation coefficient of 0.86.
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.
Since the 1990s, mobile telecommunication networks have gradually become denser around the world. Nowadays, large parts of their backhaul network consist of commercial microwave links (CMLs). Since CML signals are attenuated by rainfall, the exploitation of records of this attenuation is an innovative and an inexpensive solution for precipitation monitoring purposes. Performance data from mobile operators’ networks are crucial for the implementation of this technology. Therefore, a real-time system for collecting and storing CML power levels from the mobile phone operator “Telecel Faso” in Burkina Faso has been implemented. This new acquisition system, which uses the Simple Network Management Protocol (SNMP), can simultaneously record the transmitted and received power levels from all the CMLs to which it has access, with a time resolution of one minute. Installed at “Laboratoire des Matériaux et Environnement de l’Université Joseph KI-ZERBO (Burkina Faso)”, this acquisition system is dynamic and has gradually grown from eight, in 2019, to more than 1000 radio links of Telecel Faso’s network in 2021. The system covers the capital Ouagadougou and the main cities of Burkina Faso (Bobo Dioulasso, Ouahigouya, Koudougou, and Kaya) as well as the axes connecting Ouagadougou to these cities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.