Publication:
Application of computational intelligence methods in modelling river flow prediction: A review

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dc.citedby3
dc.contributor.authorZaini N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorYusoff M.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid23391662400en_US
dc.date.accessioned2023-05-29T06:00:01Z
dc.date.available2023-05-29T06:00:01Z
dc.date.issued2015
dc.descriptionArtificial intelligence; Arts computing; Decision making; Evolutionary algorithms; Forecasting; Fuzzy neural networks; Intelligent computing; Neural networks; Rain; Rivers; Stream flow; Support vector machines; Computational intelligence methods; Computational intelligence techniques; Computational results; Hydrological cycles; Neural networks , fuzzy logic; Prediction accuracy; River flow models; River flow prediction; Fuzzy logicen_US
dc.description.abstractRainfall and river flow are one of the most difficult elements of hydrological cycle to predict. This is due to tremendous range of variability it displays over a wide range of scale both in terms of space and time. The situation is further aggravated by the fact that rainfall-runoff is also very difficult to measure at scales of interest to hydrology and climatologic. Computational intelligence techniques provide efficient and fast results for modelling non-linear and complex data. Computational intelligence methods which inspired by the capability of learning that derive meaning from unknown relationship provide guidance for a sensible decision making. This advantage creates them adaptable and talented methods for modelling real world problems. This paper is an attempt to present the introduction to computational intelligence methods; applications to river flow modelling and its performance with regards to the parameter and method used. The methods include artificial neural networks, fuzzy logic, evolutionary computation, support vector machine; swarm intelligence and hybrid method are critically compared mainly on computational results and prediction accuracy. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7219600
dc.identifier.doi10.1109/I4CT.2015.7219600
dc.identifier.epage374
dc.identifier.scopus2-s2.0-84944400317
dc.identifier.spage370
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84944400317&doi=10.1109%2fI4CT.2015.7219600&partnerID=40&md5=244cc702665fb3597c320b98b937db66
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22287
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleI4CT 2015 - 2015 2nd International Conference on Computer, Communications, and Control Technology, Art Proceeding
dc.titleApplication of computational intelligence methods in modelling river flow prediction: A reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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