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Using Machine Learning Techniques for Rainfall Estimation Based on Microwave Links of Mobile Telecommunication Networks

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Abstract

Accurate rainfall measurements are essential for many applications including watershed management and hydrological modeling. This is particularly true in sub-Saharan Africa where environmental and hydro-meteorological hazards are recurrent. Microwave links have been proposed as an alternative or an additional source of measurement to the traditional rainfall measurement instruments. However, the estimation of rainfall based on microwave links relies on some assumptions and approximations which induce considerable differences between resulting rainfall estimations and ground observations. One of the major sources of errors in the process of estimating rainfall from microwave links is the determination of the adequate drop size distribution. Deep learning models have been recently proposed with satisfaction to improve rainfall estimates from microwave links of mobile telecommunication networks. But these models were built based on experimental setups and require large datasets. Moreover, base transceiver stations of mobile telecommunication networks operate with different signals resolutions, and a poor signal resolution increases the difficulty for data-driven approaches. In this paper, we propose to use machine learning techniques for rainfall estimation from microwave links based on existing infrastructures, in the context of information and technical restrictions from the mobile telecommunication network. Observations collected from four microwave links and two weather radars in the Netherlands (Western Europe) are used to build regression models for rainfall estimation using path integrated attenuations from these links, and received signals levels as explanatory variables. The results are satisfactory as the values of the coefficient of determination and the correlation coefficient between predictions and observations are up to 0.92 and 0.96, respectively. This proves that simple machine learning regressors can be successfully used to estimate rainfall using microwave links of mobile telecommunication networks, and they clearly outperform the empirical computational methods in the literature.

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Availability of Data and Materials

The datasets used in this work are freely available online

Code Availability

The code used to obtain the results in this study is available online

Notes

  1. 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 his availability 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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Authors and Affiliations

  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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  1. Evrad Venceslas Kamtchoum
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  2. Armand Cyrille Nzeukou Takougang
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Contributions

All 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 authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Evrad Venceslas Kamtchoum.

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Kamtchoum, E.V., Nzeukou Takougang, A.C. & Djamegni, C.T. Using Machine Learning Techniques for Rainfall Estimation Based on Microwave Links of Mobile Telecommunication Networks. SN COMPUT. SCI. 4, 46 (2023). https://doi.org/10.1007/s42979-022-01458-6

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