Abstract
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
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
| Original language | English |
|---|---|
| Pages (from-to) | 158-162 |
| Number of pages | 5 |
| Journal | International Journal on Engineering Applications |
| Volume | 3 |
| Issue number | 6 |
| Publication status | Published - 30 Nov 2015 |