Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

Farzad Fahimi, Zaher Mundher Yaseen, Ahmed El-shafie

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.

Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalTheoretical and Applied Climatology
DOIs
Publication statusAccepted/In press - 6 Feb 2016
Externally publishedYes

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hydrological modeling
artificial neural network
water resource
engineering
artificial intelligence
prediction
twentieth century

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Application of soft computing based hybrid models in hydrological variables modeling : a comprehensive review. / Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed.

In: Theoretical and Applied Climatology, 06.02.2016, p. 1-29.

Research output: Contribution to journalArticle

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