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A Machine Learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data

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Abstract

Rainfall estimation is essential for many reasons. The lack of rainfall monitoring and estimation infrastructure in some parts of the world, including sub-Saharan Africa, is a serious problem with severe consequences. For this reason, the possibility to use mobile network infrastructure, which are in continuous expansion all over the world, to monitor and estimate rainfall has been widely investigated during the last 2 decades. One of the critical tasks in the process of rainfall estimation using microwave links is the detection of rainy periods. Many methods have been proposed to realize this task in the literature, with satisfaction and underlying uncertainties resulting from mobile networks’ infrastructures and modeling assumptions. Machine learning has been proven to be very powerful for classification tasks and patterns recognition in high-dimensional data, as evidenced by many applications in various fields of science and engineering. Deep learning in particular has been recently used to improve the wet–dry periods classification task in rainfall estimation using commercial microwave links. In this paper, we propose to use other machine learning techniques for the classification of dry and wet periods using commercial microwave link data. We evaluate and compare the ability of single machine learning classifiers, ensemble machine learning classifiers, and clustering algorithms to successfully realize this task. We consider a sample of four microwave links located in the Netherlands (Western Europe), for which minimum and maximum received signal levels are recorded for each 15 min period from 30/05/2012 to 01/09/2012. In general, the considered algorithms are performant for this classification task, particularly for ensemble machine learning classifiers, among which an ensemble voting classifier emerges as a good first choice with an overall accuracy, and precision of wet periods detection very close to 100%. Its precision of dry periods’ detection is also close to 100% for the majority of the considered microwave links. These results show that machine learning classifiers are very effective for the classification of wet and dry periods for rainfall measurement using microwave links, and outperform some empirical and classical model-based methods available in the literature.

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Notes

  1. Available at: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/.

  2. Available online at https://climate4impact.eu/.

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Acknowledgements

We are very grateful to Aart Overeem and the Royal Netherlands Meteorological Institute (KNMI) for providing the radar rainfall dataset and microwave link data used in this work. We really appreciated the availability of Aart Overeem for complementary information. We are also very grateful to Dr Marielle Gosset of IRD (Institut de Recherche pour le Développement) for the facilities provided in the course of this work through the SMART and DVD (Douala Ville Durable) projects.

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  1. Department of Mathematics and Computer Science, University of Dschang, Dschang, 237, Cameroon

    Evrad Venceslas Kamtchoum

  2. Fotso Victor Institute of Technology, University of Dschang, Bandjoun, 237, Cameroon

    Armand Cyrille Nzeukou Takougang & Clémentin Tayou Djamegni

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Correspondence to Evrad Venceslas Kamtchoum.

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All the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kamtchoum Venceslas Evrad, Armand Nzeukou and Clémentin Tayou. The first draft of the manuscript was written by Kamtchoum Venceslas Evrad and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Kamtchoum, E.V., Nzeukou Takougang, A.C. & Tayou Djamegni, C. A Machine Learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data. SN COMPUT. SCI. 3, 227 (2022). https://doi.org/10.1007/s42979-022-01143-8

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