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 language | English |
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Pages (from-to) | 758-779 |
Number of pages | 22 |
Journal | Journal of Hydrology |
Volume | 562 |
DOIs | |
Publication status | Published - 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