Publication:
Flood Forecasting via Time Lag Forward Network; Kelantan, Malaysia

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dc.contributor.authorJajarmizadeh M.en_US
dc.contributor.authorMohd Sidek L.en_US
dc.contributor.authorBasri H.B.en_US
dc.contributor.authorJaffar A.S.en_US
dc.contributor.authorid55251767200en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid57189241320en_US
dc.date.accessioned2023-05-29T06:12:15Z
dc.date.available2023-05-29T06:12:15Z
dc.date.issued2016
dc.descriptionErrors; Flood control; Floods; Forecasting; Heuristic methods; Hyperbolic functions; Mean square error; Water levels; Critical issues; Flood forecasting; Flood periods; Hidden layers; Input and outputs; Optimum configurations; Root mean square errors; Water level forecasting; Weather forecasting; baseline conditions; catchment; data acquisition; dynamic property; error analysis; flood forecasting; hazard assessment; hydrological hazard; hydrological modeling; network analysis; numerical method; spatiotemporal analysis; water level; Kelantan; Malaysia; West Malaysiaen_US
dc.description.abstractForecasting water level is one of the critical issues in Malaysia for Kelantan region. Based on the flood events in 2014, this study investigates the hourly-forecasting of water level in one station namely Kg Jenob in Kelantan. For this issue, Time Lag Forward Network (TLFN) is evaluated for forecasting the water level as dynamic model. Heuristic method in stepwise forward methodology is performed. Rainfall and water level are the input and output of the modelling respectively. For selected flood period 15/12/2014 to 30/12/2014, 8 scenarios are developed to obtain a minimum error in water level forecasting. By monitoring the error, it will show that the optimum configuration of network has 2 processors in hidden layer and 7 lags have enough contribution on the result of hourly forecasting. Transfer functions in hidden and output layers are is Tangent hyperbolic and bias. Observed and simulated data are compared with usual error criteria called Mean Square Error (MSE) and Root Mean Square Error (RMSE) which obtained 0.005 and 0.07 respectively. In conclusion, this study will be as a baseline for Kelantan to show that TLFN has promising result to forecast the flood events.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12043
dc.identifier.doi10.1088/1755-1315/32/1/012043
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84966550411
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84966550411&doi=10.1088%2f1755-1315%2f32%2f1%2f012043&partnerID=40&md5=de23e20f9b6432d6189c9e5f38380124
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22786
dc.identifier.volume32
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titleFlood Forecasting via Time Lag Forward Network; Kelantan, Malaysiaen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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