Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results

Abstract

Recently, wireless telecommunication networks have become a promising alternative for rainfall measuring instruments that complement existing monitoring devices. Due to big dataset of rainfall and telecommunication networks data, empirical computational methods are less adequate representation of the actual data. Therefore, deep learning models are proposed for the analysis of big data and give more accurate representation of real measurements. In this study, we investigated rainfall monitoring results from experimental measurements and deep learning approaches such as artificial neural networks and long short-term memory. The experimental setups were in South Korea over terrestrial and satellite links, and in Ethiopia over terrestrial link for different frequency bands and link distances. The received signal level and rainfall data measurement covered four years in South Korea and the data were sampled at intervals of 10 seconds. In Ethiopia, the data were recorded over 10 months and sampled at intervals of 15 minutes. The received signal power data were used to derive the rainfall rate distribution and compared to actual rainfall measurements over the same time periods. Our results demonstrate that the proposed deep learning-based models generally have a good fit with the measured rainfall rates. The rainfall rate generated from terrestrial links was a better fit to the actual rainfall rate data than that generated from satellite links.