REVIEW OF THREE DATA-DRIVEN MODELLING TECHNIQUES FOR HYDROLOGICAL MODELLING AND FORECASTING

Various modelling techniques have been proposed and applied for modelling and forecasting of hydrological systems 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 hydrological processes, data-driven techniques however describe the behaviour of hydrological processes by taking into account only limited assumptions about the underlying 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 studies 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 modelling and forecasting studies are also discussed. The structure of this review encapsulates an introduction to each of the modelling techniques, their applications in hydrological modelling and forecasting, identification of areas of concern 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 embedded in its integration into water resources applications.