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Artificial neural network models for forecasting monthly precipitation in 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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Acknowledgments

This study was derived from the second author’s PhD thesis entitled ‘Forecasting monthly precipitation for arid regions using conditional artificial neural networks combined with Markov chain’ submitted to the Institute of Science and Technology, Istanbul Technical University, Istanbul, Turkey. We were encouraged by the constructive comments of two reviewers and Bellie Sivakumar, the guest editor, whom we deeply thank.

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Correspondence to Hafzullah Aksoy.

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Aksoy, H., Dahamsheh, A. Artificial neural network models for forecasting monthly precipitation in Jordan. Stoch Environ Res Risk Assess 23, 917–931 (2009). https://doi.org/10.1007/s00477-008-0267-x

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