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dc.contributor.authorZheng, Zhimin
dc.contributor.authorMahmood, Babar
dc.contributor.editorStormwater Conference Committee
dc.date.accessioned2018-12-18T20:51:35Z
dc.date.available2018-12-18T20:51:35Z
dc.date.issued2018-05
dc.identifier.urihttps://hdl.handle.net/10652/4435
dc.description.abstractAs we know that over the past decade or so the Artificial Intelligence (AI) techniques (e.g. ANN - Artificial Neural Network & FIS - Fuzzy Interference System) have been used as an alternative modelling tools in water resources management studies. Runoff generated from a catchment as a result of a rainfall event is a very complex hydrological process as it depends on climatological (i.e. rainfall depth, duration and intensity, etc.) and geographical (i.e. soil type, infiltration rate, evapotranspiration, etc.) factors of the catchment. The present study is about the application of Artificial Neural Network (ANN) model to forecast runoff from the Waikato River catchment areas of New Zealand. Similar to other modelling approaches, successful application of ANN is also dependant on the selection of appropriate input factors. To investigate this, the study applied three different approaches for the selection of appropriate input vectors to be used for the ANN model. The study demonstrated that ANN can successfully forecast the runoff generated from a catchment using antecedent rainfall and runoff data series identified on the basis of cross-correlation and auto-correlation coefficients. The ANN models developed using three approaches (i.e. sequential, pruned and non-sequential time series) were able to predict runoff generated from the Waikato River catchment using antecedent rainfall/runoff data. The study showed that the ANN models were sensitive to the selection of appropriate input vector. The ANN model developed using the nonsequence approach performed well, and gave the highest R2 and NSE values (i.e. 97-98 %) during the validation and testing phases of this modelling exerciseen_NZ
dc.language.isoenen_NZ
dc.relation.urihttps://www.waternz.org.nz/Article?Action=View&Article_id=1501en_NZ
dc.subjectNew Zealanden_NZ
dc.subjectWaikato River (N.Z.)en_NZ
dc.subjectArtificial Neural Network (ANN)en_NZ
dc.subjectartificial intelligence (AI)en_NZ
dc.subjectwater catchmentsen_NZ
dc.subjectcatchment runoff modelling.en_NZ
dc.titleApplication of Artificial Neural Network model to forecast runoff for Waikato river catchmenten_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.date.updated2018-12-12T13:30:09Z
dc.subject.marsden090509 Water Resources Engineeringen_NZ
dc.identifier.bibliographicCitationZhimin, Z., & Mahmood, B. (2018). Application of Artificial Neural Network model to forecast runoff for Waikato river catchment. In Stormwater Conference Committee (Ed.), Proceedings of Stormwater Conference 2018 – Wai Ora – Rising to the Challenge (pp. Online). Retrieved from https://www.waternz.org.nz/Article?Action=View&Article_id=1501en_NZ
unitec.publication.spageOnlineen_NZ
unitec.conference.titleProceedings of Stormwater Conference 2018 – Wai Ora – Rising to the Challengeen_NZ
unitec.conference.orgWater New Zealanden_NZ
unitec.conference.locationQueenstown, New Zealanden_NZ
unitec.conference.sdate2018-05-23
unitec.conference.edate2018-05-25
unitec.peerreviewedyesen_NZ
unitec.identifier.roms62815en_NZ


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