Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

Hafzullah Aksoy*, Ahmad Dahamsheh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

Original languageEnglish
Pages (from-to)758-779
Number of pages22
JournalJournal of Hydrology
Volume562
DOIs
Publication statusPublished - Jul 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

  • Arid region
  • Artificial neural networks
  • Intermittent precipitation
  • Markov chain
  • Synthetic data
  • Thomas-Fiering model

Fingerprint

Dive into the research topics of 'Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions'. Together they form a unique fingerprint.

Cite this