Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation
Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation
复制标题
使用人工神经网络进行雨衰预测以缓解动态雨衰
DOI：
10.23919/saiee.2019.8643146
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发表时间：
2019
影响因子：
1.4
通讯作者：
A. Alonge
中科院分区：
文献类型：
--
作者：
M. Ahuna;T. Afullo;A. Alonge
形成降雨的大气过程很复杂，无法使用数学或统计模型准确预测。在本文中，反向传播神经网络 (BPNN) 经过训练来预测降雨率，从而预测链路上可能出现的衰减。这项研究是在南非德班亚热带地区（29.8587°S，31.0218°E）进行的。利用输入和输出之间的非线性映射功能，使用 2013 年至 2016 年收集的降雨数据训练反向传播神经网络，以预测降雨率。将预测降雨率得出的长期雨衰统计数据与实际和 ITU-R 模型进行比较，结果表明，预测雨衰超出平均年份 0.01% 的误差范围相对较小。此外，对不同降雨状况的单个降雨事件中的预测和实际雨衰进行了分析，结果表明所提出的模型可用于预测链路状态。当使用 2017 年 1 月至 2018 年 5 月（涵盖夏、秋、冬、春四个不同气候季节）收集的未见过的数据对经过训练的 BPNN 进行测试时，就证明了这一点。检验结果显示相关系数为0.8298。最后，利用热带地区卢旺达布塔雷（2.6078°S，29.7368°E）的降雨数据对所提出的降雨预测模型进行了测试，所得结果表明该模型对其他地区的可移植性。
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 sub-tropical 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.