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Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

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Abstract

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers—whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)—for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5–20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

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Abbreviations

ANN:

artificial neural network

WA:

whale algorithm

BA:

bat algorithm

PSO:

particle swarm algorithm

SSL:

suspended sediment load

RMSE:

root mean square error

SVM:

support vector machine

ANFIS:

adaptive neurofuzzy interface system

GP:

genetic programming

WLSSVM:

wavelet least square support vector machine

RBFNN:

radial basis function neural network

FFNN:

feedforward neural network

SSC:

suspended sediment concentration

MLP:

multilayer perceptron

WOA:

whale optimisation algorithm

NDA:

non-dominated

AR:

archive

PF:

Pareto front

MAE:

mean absolute error

PS:

population size

MAF:

maximum frequency

MAL:

maximum loudness

MIL:

minimum loudness

NSE:

Nash-Sutcliff efficiency

RSR:

the RMSE observation standard ratio

PBIAS:

percent bias

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Acknowledgements

The authors would like to appreciate the financial support received from the Institute of Postgraduate Studies and Research (IPSR) of Universiti Tunku Abdul Rahman.

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Authors and Affiliations

  1. Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Mohammad Ehteram

  2. Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia

    Ali Najah Ahmed

  3. Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia

    Sarmad Dashti Latif

  4. Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia

    Yuk Feng Huang

  5. Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Meysam Alizamir

  6. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  7. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Ozgur Kisi

  8. Faculty of Computer Technologies and Engineering, International Black Sea University, Tbilisi, Georgia

    Cihan Mert

  9. Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia

    Ahmed El-Shafie

  10. National Water Center, United Arab Emirates University (UAEU), 15551, Al Ain, United Arab Emirates

    Ahmed El-Shafie

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  1. Mohammad Ehteram
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Correspondence to Yuk Feng Huang.

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Ehteram, M., Ahmed, A.N., Latif, S.D. et al. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environ Sci Pollut Res 28, 1596–1611 (2021). https://doi.org/10.1007/s11356-020-10421-y

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