Artificial neural network models for forecasting monthly precipitation in Jordan |
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Authors: | Hafzullah Aksoy Ahmad Dahamsheh |
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Institution: | (1) Department of Civil Engineering, Hydraulics Division, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey;(2) Jordan Meteorological Department, Amman, 11134, Jordan |
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Abstract: | Forecasting precipitation in arid and semi-arid regions, in Jordan in the Middle East for example, has particular importance
since precipitation is the unique source of water in such regions. In this study, 1-month ahead precipitation forecasts are
made using artificial neural network (ANN) models. Feed forward back propagation (FFBP), radial basis function (RBF) and generalized
regression type ANNs are used and compared with a simple multiple linear regression (MLR) model. The models are tested on
monthly total precipitation recorded at three meteorological stations (Baqura, Amman and Safawi) from different climatological
regions in Jordan. For the three stations, it is found that the best calibrated model is FFBP with respect to all performance
criteria used in the study, including determination coefficient, mean square error, mean absolute error, the slope and the
intercept in the best-fit linear line of the scatter diagram. In the validation stage, FFBP is again the best model in Baqura
and Amman. However, in Safawi, the driest station, not only FFBP but also RBF and MLR perform equally well depending on the
performance criterion under consideration. |
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Keywords: | |
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