RNN Models for Rain Detection

Abstract

The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions