:: Volume 7, Issue 3 (12-2019) ::
jgit 2019, 7(3): 57-77 Back to browse issues page
Combining Neural Network with Genetic Algorithm for prediction of S4 Parameter using GPS measurement
Ali Reza Atabati, Mohammad Mahdi Alizadeh *
K.N. Toosi University of Technology
Abstract:   (1952 Views)
  The ionospheric plasma bubbles cause unpredictable changes in the ionospheric electron density. These variations in the ionospheric layer can cause a phenomenon known as the ionospheric scintillation. Ionospheric scintillation could affect the phase and amplitude of the radio signals traveling through this medium. This phenomenon occurs frequently around the magnetic equator and in low latitudes, mid as well as high latitude regions. ionospheric scintillation is a very complex phenomenon to be modeled. Patterns of ionospheric scintillation occurrence are depended on spatial and temporal ionospheric variabilities. Neural Network (NN) is a data-dependent method, that its performance improves with the sample size. According to the advantages of NN for large datasets and noisy data, the NN model has been implemented for predicting the occurrences of amplitude scintillations. In this paper, the GA technique was considered to obtain primary weights in the NN model in order to identify appropriate S4 values for GUAM GPS station in Guam country (latitude: 144.8683, Longitude:13.5893). The modeling was carried out for the whole month of June 2017, while this model along with ionospheric physical data was used for predicting ionospheric scintillation at the first day of July 2017, the day after the modeling. The designed model has the ability to predict daily ionospheric scintillation with the accuracy of about 78%.
Keywords: Neural Networks, Genetic Algorithm, Ionospheric Scintillation
Full-Text [PDF 1819 kb]   (1616 Downloads)    
Type of Study: Research | Subject: Geodesy
Received: 2018/12/29 | Accepted: 2019/02/2 | Published: 2019/12/21

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Volume 7, Issue 3 (12-2019) Back to browse issues page