Ensemble machine learning paradigms in hydrology: A review

Mohammad Zounemat-Kermani, Okke Batelaan, Marzieh Fadaee, Reinhard Hinkelmann

    Research output: Contribution to journalReview articlepeer-review

    54 Citations (Scopus)


    Recently, there has been a notable tendency towards employing ensemble learning methodologies in assorted areas of engineering, such as hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available for implementation in hydrological sciences has led to the development and utilization of different strategies in the implementation. This review paper explores and refers to the advancement of ensemble methods, including the resampling ensemble methods (e.g., bagging, boosting, and dagging), model averaging, and stacking viz. generalized stacked, in different application fields of hydrology. The main hydrological topics in this review study cover subjects such as surface hydrology, river water quality, rainfall-runoff, debris flow, river icing, sediment transport, groundwater, flooding, and drought modeling and forecasting. The general findings of this survey demonstrate the absolute superiority of using ensemble strategies over the regular (individual) model learning in hydrology. In addition, the boosting techniques (e.g., boosting, AdaBoost, and extreme gradient boosting) have been more frequent and successfully implemented in hydrological problems than the bagging, stacking, and dagging approaches.

    Original languageEnglish
    Article number126266
    Number of pages13
    JournalJournal of Hydrology
    Publication statusPublished - Jul 2021


    • Committee machine
    • Data mining
    • Hydroinformatics
    • Random forest
    • Soft computing


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