Rainfall-based river flow prediction using NARX in Malaysia

Hassanuddin Mohamed Noor, David Ndzi, Guangguang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Flood forecasting is one of the most important and demanding operational responsibilities carried out by meteorological services all over the world. This task is complicated in the field of meteorology because all decisions have to consider in the visage of physiographical uncertainty factors such as the land coverage and vegetation, type of soil and topology of the catchment area [1][2]. This paper shows that the Nonlinear Autoregressive Exogenous (NARX) model can successfully to model a flow of the rivers 24 hours in advance based on current rainfall rates.
Original languageEnglish
Title of host publication2017 IEEE 13th International Colloquium on Signal Processing & its Applications
PublisherIEEE
Pages67-72
ISBN (Electronic)978-1509011841
ISBN (Print)978-1509011858
DOIs
Publication statusPublished - 12 Oct 2017
Event2017 IEEE 13th International Colloquium on Signal Processing & its Applications - Batu Ferringhi, Malaysia
Duration: 10 Mar 201712 Mar 2017

Conference

Conference2017 IEEE 13th International Colloquium on Signal Processing & its Applications
Abbreviated titleCSPA
Country/TerritoryMalaysia
CityBatu Ferringhi
Period10/03/1712/03/17

Keywords

  • rivers
  • floods
  • predictive models
  • artifical neural networks
  • training
  • reservoirs
  • neurons
  • artificial neural network (ANN)
  • flow rate prediction

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