Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

Fatemeh Barzegari Banadkooki, Mohammad Ehteram, Ali Najah Ahmed, Fang Yenn Teo, Mahboube Ebrahimi, Chow Ming Fai, Yuk Feng Huang, Ahmed El-Shafie

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.

Original languageEnglish
Pages (from-to)38094-38116
Number of pages23
JournalEnvironmental Science and Pollution Research
Volume27
Issue number30
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes

Keywords

  • Ant lion optimization
  • Artificial neural network
  • Bat algorithm
  • Particle swarm optimization
  • River suspended sediment load
  • Sensitivity analysis

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