Author(s): Kadiri Kamoru, Keshinro Kazeem Kolawole, Omotayo Mayowa and Enem Theophilus

Abstract: The predicted precipitation accuracy in light of the current global climate change is very important. The Back-Propagation Neural Network (BPNN) technique then utilized the Artificial Neural Network (ANN) to correctly forecast rainfall. The study is based on data from three Nigerian terrestrial microwave connections operating at frequencies of 23 and 38 GHz. At 23 GHz, a normal distribution with an average of zero is the best represented fade slope distribution, contrary to the model ITU-R earth to satellite rain fade slope. Based on the data analysis observed at 23 GHz, a novel prediction model is developed. The suggested model is validated using 38 GHz fade slope data and shown to work well with nearly all attenuation levels. A chi-square fitness test is used to further validate the model. The suggested model will be critical in the development of rainfall mitigation methods for tropical terrestrial connections. The findings of the research indicate that BPNN models may be utilized as a prediction algorithm that offers a high predictive accuracy of three types of design: 500, 1000 and 1,500.The experiment examined data on precipitation using the BPNN Architecture's two-hidden layers, from three periods of time [2-50-10-1, 500]. The average square error is used for categorization work performance evaluation. The results of the experiment indicate that the design of the 100-year-old [2-50-20-1, MSE of 0 00096341] is excellent. Moreover, the BPNN algorithm provides an efficient Southwestern Nigeria predictor model.

DOI: 10.33545/27076571.2022.v3.i1a.40

Pages: 10-15 | Views: 508 | Downloads: 241

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