Application of Neural Network in Atmospheric Refractivity Profile at Makurdi

Agbo, G. A.; Ibeh, G. F.; Onah, D.U; Umahi, A. E.; Nnaji, E; Ugwuonah, F.C

doi:10.2991/icibet.2013.107

Cited by 3 publications

(2 citation statements)

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“…The radio refractivity relies on the absolute air temperature, T (K); vapor pressure, e (mbar); and pressure, p (mbar). The refractivity N can be calculated with the help of the following formula [ 6 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…They have many practical fields of application, for instance, system identification and process control, resource management, quantum chemistry, financial applications, medical diagnoses, decision-making, and data mining [ 31 ]. Various researchers have already applied ANNs in the prediction of meteorological parameters, [ 9 , 32 ]. Although the proposed technique was implemented on a smart grid [ 33 ], it remains a novel method for the prediction of meteorological parameters and consequently, radio refractivity.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Predicting radio refractivity using a stochastic model

Ogunjo

Ajewole

Adediji

et al. 2014

2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS)

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Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models

Etu

Oyedepo

2018

JCEST

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Evidence from literature has shown the absence of the use of Artificial Neural Network techniques in formulating trip generation forecasts in Nigeria, rather the practice has consisted more on use of regression techniques. Therefore, in this study, the accuracy of Radial Basis Function Neural Network (RBFNN) and Multiple Linear Regression model (MLR) in formulating home-based trips generation forecasts was assessed. Datasets for the study were acquired from a household travel survey in the high density zones of Akure, Nigeria and were analysed using SPSS 22 statistical software. Results of data analysis showed that the RBFNN model with higher Coefficient of Determination (R2) value of 0.913 and lower Mean Absolute Percentage Error (MAPE) of 0.421 performed better than the MLR with lower R2 value of 0.552 and higher MAPE of 0.810 in predicting the number of home-based trips generated in the study area. The study demonstrated the higher accuracy of the RBFNN in producing trip generation forecasts in the study area and is consequently recommended for researchers in executing such forecasts.

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Application of Neural Network in Atmospheric Refractivity Profile at Makurdi

Cited by 3 publications

References 0 publications

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

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

Predicting radio refractivity using a stochastic model

Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models

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