Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation

Abstract

Atmospheric processes from which rainfall is formed are complex and cannot be accurately predicted using mathematical or statistical models. In this paper, the backpropagation neural network (BPNN) is trained to predict rainfall rates, and hence attenuation that is likely to be experienced on a link. This study is carried out over the subtropical region of Durban, South Africa (29.8587°S, 31.0218°E). Utilizing the non-linear mapping capability between inputs and outputs, the backpropagation neural network is trained using rainfall data collected from 2013 to 2016 to predict rainfall rates. Long-term rain attenuation statistics arising from predicted rain rates are compared with actual and ITU-R model, and results show a relatively small margin of error between predicted rain attenuation exceeded for 0.01 % of an average year. Furthermore, analysis of predicted and actual rain attenuation within individual rain events from different rainfall regimes was carried out and results show that the proposed model can be used to predict the state of the link. This is demonstrated when the trained BPNN was tested using unseen data that was collected from January 2017 to May 2018, a period that spans through all four different climatic seasons of summer, autumn, winter and spring. Results of the test show a correlation coefficient of 0.8298. Finally, the proposed rain prediction model was tested on rainfall data from Butare, Rwanda (2.6078°S, 29.7368°E), which is a tropical region and results obtained indicate the portability of the proposed model to other regions.

A Survey of Rain Fade Models for Earth–Space Telecommunication Links—Taxonomy, Methods, and Comparative Study

“…Empirical models SAM model [32] Singapore [33] Garcia [34] GSST [66] LU model [68] Karasawa [88] Breakpoint [29] Unified [89] YLO model [12] Statistical models M-K model [90] MARIMA [91] ITU-R P.618 [45] PEARP [92] Das Model 1 [93] Physical models Bryant [37] Crane TC [94] PM Model [95] SC EXCELL [62] Fade models Japan [96] Das model 2 [97] ITU-R P.1623 [98] Dao model [99] Learning-based AANN [100] The SAM model [32] has evolved through some previously published articles [101].…”

Learning-Assisted Rain Attenuation Prediction Models

“…[6] Feedforward back-propagation neural network (FFBNN) Feedforward back-propagation neural network [8] Supervised machine learning (SML) SVM and SML-based regression algorithm that uses Gaussian process-compatible kernel functions. [10] Artificial neural network (ANN) and k-nearest neighbor (kNN) kNN and ANN (recurrent neural network) [11] Back-propagation neural network (BPNN) BPNN with a sigmoid input function [12] Feed-forward multilayer perceptron (FFMLP) in addition to supervised learning (SL)…”

“…MN Ahuna et al [11] developed a rainfall forecast model based on a back-propagation neural network (BPNN) with four years of data collected. The model's predicted results matched with two years of real rain rate from 2017 to 2018.…”

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

“…With the development of machine learning, it is gradually being applied to precipitation observation [23][24][25][26][27]. To the best our knowledge, the earliest application of artificial neural network (ANN) to rainfall estimation can be traced back to 1992 when French et al used current data to forecast rainfall an hour later by back propagation (BP) neural network [23].…”

“…To the best our knowledge, the earliest application of artificial neural network (ANN) to rainfall estimation can be traced back to 1992 when French et al used current data to forecast rainfall an hour later by back propagation (BP) neural network [23]. Similar rainfall measurement method was tested in many regions by Ahuna et al and showed reliable performances [26]. In addition, Michaelides et al used the ANN to fill up missing rainfall data [24], and Lathifah et al identified different precipitation categories based on classification and regression tree (CART) [27].…”