A modified artificial neural network based prediction technique for tropospheric radio refractivity

Shumaila Javeed; Khurram Saleem Alimgeer; Wajahat Javed; M Atif; Mueen Uddin

doi:10.1371/journal.pone.0192069

A modified artificial neural network based prediction technique for tropospheric radio refractivity

PLoS One. 2018 Mar 1;13(3):e0192069. doi: 10.1371/journal.pone.0192069. eCollection 2018.

Authors

Shumaila Javeed¹, Khurram Saleem Alimgeer², Wajahat Javed², M Atif³, Mueen Uddin⁴

Affiliations

¹ Department of Mathematics, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, Pakistan.
² Department of Electrical Engineering, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, Pakistan.
³ Department of Physics and Astronomy, College of Science, King Saud University, Riyadh, Saudi Arabia.
⁴ Department of Information Systems, Faculty of Engineering, Effat University, Jeddah, Saudi Arabia.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Atmosphere
Humidity
Models, Chemical
Neural Networks, Computer*
Pressure
Radio Waves*
Radio* / instrumentation
Temperature

Grants and funding

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for its funding of this research through the Research Group Project No. RGP-VPP-293.