ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

Haitham Abdulmohsin Afan, Ahmed El-Shafie, Zaher Mundher Yaseen, Mohammed Majeed Hameed, Wan Hanna Melini Wan Mohtar, Aini Hussain

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.

Original languageEnglish
JournalWater Resources Management
DOIs
Publication statusAccepted/In press - 12 Nov 2014

Fingerprint

artificial neural network
Sediments
Neural networks
prediction
sediment
Feedforward neural networks
Water resources
water resource
Rivers
engineering
river
modeling

Keywords

  • Feed forward neural network
  • Radial basis function
  • Sediment load

ASJC Scopus subject areas

  • Water Science and Technology
  • Civil and Structural Engineering

Cite this

ANN Based Sediment Prediction Model Utilizing Different Input Scenarios. / Afan, Haitham Abdulmohsin; El-Shafie, Ahmed; Yaseen, Zaher Mundher; Hameed, Mohammed Majeed; Wan Mohtar, Wan Hanna Melini; Hussain, Aini.

In: Water Resources Management, 12.11.2014.

Research output: Contribution to journalArticle

Afan, Haitham Abdulmohsin ; El-Shafie, Ahmed ; Yaseen, Zaher Mundher ; Hameed, Mohammed Majeed ; Wan Mohtar, Wan Hanna Melini ; Hussain, Aini. / ANN Based Sediment Prediction Model Utilizing Different Input Scenarios. In: Water Resources Management. 2014.
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