Abstract
Accurate and real time rainfall levels estimations are very useful in various applications of hydraulic structure design, agriculture, weather forecasting, climate modeling, etc. An accurate measurement of rainfall with high spatial resolution is possible with an appropriate positioned set of rainfall gauge, but an alternative method to estimate the rainfall is the analysis of electromagnetic wave, in particular the microwave attenuation. Mainly this is done concerning impact of rain on transmission of electromagnetic waves at the level of radio frequency above 10 GHz. In this paper we investigate a new method to estimate rainfall level using the analysis of received signal strength and its variance in mobile LTE/4G terminal to produce a map of prediction.
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Beritelli, F., Capizzi, G., Lo Sciuto, G., Scaglione, F., Połap, D., Woźniak, M. (2017). A Neural Network Pattern Recognition Approach to Automatic Rainfall Classification by Using Signal Strength in LTE/4G Networks. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_36
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DOI: https://doi.org/10.1007/978-3-319-60840-2_36
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