Artificial neural network models for forecasting monthly precipitation in Jordan


Aksoy H. , DAHAMSHEH A.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, cilt.23, ss.917-931, 2009 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 23 Konu: 7
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1007/s00477-008-0267-x
  • Dergi Adı: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • Sayfa Sayıları: ss.917-931

Özet

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.