Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Ahmad Dahamsheh, Hafzullah Aksoy*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Forecasting monthly precipitation in arid regions is investigated by means of feed forward back propagation (FFBP) artificial neural networks (ANNs) and compared to the linear regression technique with multiple inputs (MLR). Four meteorological stations from different geographical regions in Jordan are selected. The ANNs and MLR processes are analysed based on the mean square error, relative/absolute error, determination coefficient as well as the central statistical moments such as mean, standard deviation, and minimum and maximum values. It is found that whilst on one hand the ANNs are slightly better than the MLR in forecasting the monthly total precipitation, on the other hand, both are found with to have limitations which should be improved by means of either changing the type and architecture of the ANNs or incorporating modelling tools such as Markov chains into the forecast model.

Original languageEnglish
Pages (from-to)325-337
Number of pages13
JournalMeteorological Applications
Volume16
Issue number3
DOIs
Publication statusPublished - Sept 2009

Keywords

  • Artificial neural networks
  • Feed-forward back propagation
  • Intermittent precipitation
  • Jordan
  • Linear regression
  • Monthly precipitation

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