Low complexity single-layer neural network for enhanced rainfall estimation using microwave links

Abstract

A low complexity accurate model for precipitation estimation is crucial for monitoring several hydrological and water resource applications. Based on the R-k empirical power-law relation described by the P.838-3 ITU recommendation, rainfall rate can be predicted based on specific attenuation of microwave links. The accuracy of this method is impacted by several ambiguities and errors. In order to overcome these limitations, numerous highly complex pre-treatment and post-processing methods should be used. As an alternative method of low complexity, a supervised learning algorithm using a single-layer neural network (the perceptron) is suggested in this paper. Optimal weights parameters were obtained based on the minimization of the mean square error (MSE). A case study was carried out using 40 days of data gathered from two commercial microwave links (CMLs) and one rain gauge. Experimental results showed that this machine learning-supervised approach performed better than the R-k-based method. The mean square error of the path-averaged rainfall rate was reduced from 0.13 mm2 h-1 to 0.08 mm2 h-1 for training data, and from 0.2 mm2 h-1 to 0.1 mm2 h-1 for test data. This promising alternative method for rainfall estimation could considerably improve the efficiency of many applications, such as those developed for real-time urban flood risk management.