Department of Mathematics, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, Pakistan.
Department of Electrical Engineering, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, Pakistan.
PLoS One. 2018 Mar 1;13(3):e0192069. doi: 10.1371/journal.pone.0192069. eCollection 2018.
Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communication system interrupts. In this work, a modified artificial neural network (ANN) based model is applied to predict the refractivity. The suggested ANN model comprises three modules: the data preparation module, the feature selection module, and the forecast module. The first module applies pre-processing to make the data compatible for the feature selection module. The second module discards irrelevant and redundant data from the input set. The third module uses ANN for prediction. The ANN model applies a sigmoid activation function and a multi-variate auto regressive model to update the weights during the training process. In this work, the refractivity is predicted and estimated based on ten years (2002-2011) of meteorological data, such as the temperature, pressure, and humidity, obtained from the Pakistan Meteorological Department (PMD), Islamabad. The refractivity is estimated using the method suggested by the International Telecommunication Union (ITU). The refractivity is predicted for the year 2012 using the database of the previous ten years, with the help of ANN. The ANN model is implemented in MATLAB. Next, the estimated and predicted refractivity levels are validated against each other. The predicted and actual values (PMD data) of the atmospheric parameters agree with each other well, and demonstrate the accuracy of the proposed ANN method. It was further found that all parameters have a strong relationship with refractivity, in particular the temperature and humidity. The refractivity values are higher during the rainy season owing to a strong association with the relative humidity. Therefore, it is important to properly cater the signal communication system during hot and humid weather. Based on the results, the proposed ANN method can be used to develop a refractivity database, which is highly important in a radio communication system.
无线电折射在开发和设计无线电系统以达到最佳性能水平方面发挥着重要作用。对流层中的折射是影响电磁波的特征之一，因此通信系统会中断。在这项工作中，应用了一种基于改进人工神经网络 (ANN) 的模型来预测折射。所提出的 ANN 模型由三个模块组成：数据准备模块、特征选择模块和预测模块。第一个模块应用预处理使数据与特征选择模块兼容。第二个模块从输入集中丢弃不相关和冗余的数据。第三个模块使用 ANN 进行预测。ANN 模型应用了一个 S 型激活函数和一个多变量自回归模型，以便在训练过程中更新权重。在这项工作中，根据从巴基斯坦气象局（PMD）获得的十年（2002-2011 年）气象数据，如温度、压力和湿度，预测和估计折射。折射是使用国际电信联盟 (ITU) 建议的方法来估计的。借助 ANN，使用前十年的数据库来预测 2012 年的折射。ANN 模型在 MATLAB 中实现。接下来，将估计的和预测的折射水平相互验证。大气参数的预测和实际值（PMD 数据）彼此吻合良好，证明了所提出的 ANN 方法的准确性。进一步发现，所有参数与折射都有很强的关系，特别是温度和湿度。由于与相对湿度有很强的关联，因此在雨季折射值较高。因此，在炎热潮湿的天气中，正确满足信号通信系统非常重要。基于这些结果，可以使用所提出的 ANN 方法来开发折射数据库，这在无线电通信系统中非常重要。
