Please use this identifier to cite or link to this item: http://hdl.handle.net/10321/2381
Title: Review of three data- driven modelling techniques for hydrological modelling and forecasting
Authors: Oyebode, Oluwaseun Kunle 
Otieno, Fredrick Alfred O. 
Adeyemo, Josiah 
Keywords: Data-driven models;Fuzzy rule-based systems;Hydrological mod-elling and forecasting;K-nearest neighbours;Model trees
Issue Date: 2014
Publisher: PSP
Source: Oyebode, 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.
Abstract: Various 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.
URI: http://hdl.handle.net/10321/2381
ISSN: 1018-4619
Appears in Collections:Research Publications (Engineering and Built Environment)

Files in This Item:
File Description SizeFormat 
Oyebode_FEB_23_7_2014.pdf509.62 kBAdobe PDFThumbnail
View/Open
Show full item record

Page view(s)

70
checked on Jan 20, 2018

Download(s)

141
checked on Jan 20, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.