Rainfall rate prediction based on artificial neural networks for rain fade mitigation over earth-satellite link

Ahuna, Mary N.; Afullo, Thomas J.; Alonge, Akintunde

doi:10.1109/afrcon.2017.8095546

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…where Oa is the actual output, Ot is the desired output (target) and H is the number of data points. Errors in 3are minimized using an error derivative given by [8,14]:…”

Section: -765mentioning

confidence: 99%

“…a rainfall storm event. Many researchers, including [5][6][7][8], have used an artificial neural network (ANN) for rainfall forecasting, and showed that the ANN can give acceptable results after training. In this paper, a backpropagation neural network (BPNN) is used to predict and classify rain attenuation for dynamic rain attenuation mitigation.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is a detailed extension of work originally reported in [8]. In the current paper, additional material include: (1) retraining the BPNN with complete 2016 data, contrary to [8] where some data was set aside for testing and validation of the training network; (2) generation of CCDFs for predicted rain rates and rain attenuation, and (3) classification of predicted rain attenuation. The paper is structured as follows: Section 2 gives a summary of related work whereas Section 3 describes the artificial neural network as a computing unit.…”

Section: Introductionmentioning

confidence: 99%

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Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation

Ahuna¹,

Afullo²,

Alonge³

2019

SAIEE Afr. Res. J.

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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 subtropical 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.

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“…where Oa is the actual output, Ot is the desired output (target) and H is the number of data points. Errors in 3are minimized using an error derivative given by [8,14]:…”

Section: -765mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation

Ahuna¹,

Afullo²,

Alonge³

2019

SAIEE Afr. Res. J.

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“…The technique used in this method is pattern recall that considers historical rainfall rate patterns over Durban. For this model verification, RMSE method is used [13]. Mr. Chetan C. Janbandhu, Prof. Praful D. Meshram, Prof. Madhuri N. Gedam use a Bayesian approach to predict the rainfall and accuracy by comparing the different size of the training dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this paper, the FFNN is used to predict the rainfall in advance. Feed Forward Neural Network is also called Multilayer Perceptron(MLP) [13].In which the information flows in only forward, this type of network creates a multilayer network. The neurons in the layer perform independent computation in input and passthrough hidden layer to the output layer [3].…”

Section: B Multi-layer Perceptronmentioning

confidence: 99%

Soft Computing Research For Weather Prediction Using Multilayer Architecture

Shah¹,

Meganathan²,

Kamali³

2019

IJEAT

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Rainfall prediction is helpful for the agriculture sector. Early prediction of drought and torrent situations is achieved through time series data. For the precise prediction, Artificial Neural Network(ANN) technique is used. The rainy dataset is tested using Feed Forward Neural Network(FFNN). The performance of this model is evaluated using Mean Square Error(MSE) and Magnitude of Relative Error(MRE). Better performance achieved when compared with other data mining techniques.

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Fade Slope Prediction Model for Rain Storms over Sub-tropical Africa

Ahuna

Afullo

2019

2019 Ieee Africon

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Rainfall rate prediction based on artificial neural networks for rain fade mitigation over earth-satellite link

Cited by 17 publications

References 10 publications

Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation

Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation

Soft Computing Research For Weather Prediction Using Multilayer Architecture

Fade Slope Prediction Model for Rain Storms over Sub-tropical Africa

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