Oyebode, Oluwaseun KunleOtieno, Fredrick Alfred O.Adeyemo, Josiah2017-03-132017-03-132014Oyebode, O., Otieno, F. and Adeyemo, J. 2014. Review of three data- driven modelling techniques for hydrological modelling and forecasting. Fresenius Environmental Bulletin 23(7):1443-1454.1018-4619http://hdl.handle.net/10321/2381Various modelling techniques have been proposed and applied for modelling and forecasting of hydrological sys-tems in different studies. These modelling techniques are majorly categorized into two namely, process-based and data-driven modelling techniques. While the process-based techniques provides detailed description of hydro-logical processes, data-driven techniques however de-scribe the behaviour of hydrological processes by taking into account only limited assumptions about the underly-ing physics of the system being modelled. Although, process-based techniques have been widely applied in numerous hydrological modelling studies, the application of data-driven modelling techniques on the other hand has not been fully embraced in the hydrological domain. This paper provides a comprehensive review of several stud-ies relating to three data-driven modelling techniques namely, K-Nearest Neighbours (K-NN), Model Trees (MTs) and Fuzzy Rule-Based Systems (FRBS). Modern trends with respect to their applications in hydrological model-ling and forecasting studies are also discussed. The struc-ture of this review encapsulates an introduction to each of the modelling techniques, their applications in hydrological modelling and forecasting, identification of areas of con-cern in their use, performance improvement methods, as well as summary of their advantages and disadvantages. The review aims to make a case for the application of data-driven modelling techniques by discussing the benefits em-bedded in its integration into water resources applications.12 penData-driven modelsFuzzy rule-based systemsHydrological mod-elling and forecastingK-nearest neighboursModel treesArticle