Daily rainfall forecasting with Deep ESN neural network based on experimental data from meteorological station in Hormozgan province

Document Type : Original Article

Authors

1 engineering faculty, Computer group Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran.

2 engineering faculty, Ù‹Rafsanjan Branch, Islamic Azad University, Rafsanjan, Iran.

3 engineering faculty, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran.

Abstract

In this study, the accuracy of daily rain prediction by two methods including DeepESN neural network and Linear multivariate regression has been compared. For this reason, actual data for rain and other affective parameters by daily distance time and thirty years has been received from the Hormozgan climate organ and has been analyzed by two methods. This data is from Bandar Abbas, Minab, and Qeshm cities, and because of short distance of these cities before entering data to DeepESN neural network and linear multivariate regression has been averaged. Demodulation for DeepESN in Matlab and linear multivariate regression in SPSS has been done. At the end of processing, results show that the daily rain forecasting model in DeepESN neural network has higher accuracy compared with the model produced with linear multivariate regression and have received better results for the DeepESN forecasting model by using estimation functions.

Keywords

References

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Volume 2, Issue 1 - Serial Number 1
Spring 2023
March 2022
Pages 31-37

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