Rainfall Prediction in Semi-Arid Regions in Jordan Using Back Propagation Neural Networks

Bilal Zahran(1*), Abdelwadood Mesleh(2), Mohammed Matouq(3), Omar AlHeyasat(4), Tariq Alwada'n(5)

(1) Computer Engineering Department, Faculty of Engineering Technology, Al-Balqa Applied University, Jordan
(2) Computer Engineering Department, Faculty of Engineering Technology, Al-Balqa Applied University, Jordan
(3) Computer Engineering Department, Faculty of Engineering Technology, Al-Balqa Applied University, Jordan
(4) Computer Engineering Department, Engineering College, Al-Balqa Applied University, Jordan
(5) Computer Science Department, Faculty of Information Technology, The World Islamic Sciences and Education University, Jordan
(*) Corresponding author

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In Jordan, agriculture and irrigation depend highly on rainfalls. Rainfall prediction is a challenging area of investigation for scientists. In this paper, a precipitation prediction model using artificial neural networks (ANNs) is proposed. The seasonal amount of rainfall in several areas in Jordan is predicted using rainfall rate time-series data, these rainfall rate data has been recorded from 26 stations located in different areas in Jordan. A feed forward ANN based on backpropagation (BP) is designed and trained to predict the future rainfalls in Jordan. Results are encouraging and accurate for rainfall prediction in Jordan.
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Precipitation; Seasonal Rainfall; Back Propagation; Neural Networks; Climate Change; Jordan

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Department of Meteorology in Jordan: www.jometeo.gov.jo.

Royal Jordanian geographic center: www.rjgc.gov.jo.

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