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Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag time...

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Main Authors: Darabi, Hamid, Mohamadi, Sedigheh, Karimidastenaei, Zahra, Kisi, Ozgur, Ehteram, Mohammad, ELShafie, Ahmed, Torabi Haghighi, Ali
Format: Article
Published: Springer 2021
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QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/28830/
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spelling my.um.eprints.288302022-04-21T06:54:11Z http://eprints.um.edu.my/28830/ Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models Darabi, Hamid Mohamadi, Sedigheh Karimidastenaei, Zahra Kisi, Ozgur Ehteram, Mohammad ELShafie, Ahmed Torabi Haghighi, Ali QA75 Electronic computers. Computer science Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine-cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash-Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models. Springer 2021-06 Article PeerReviewed Darabi, Hamid and Mohamadi, Sedigheh and Karimidastenaei, Zahra and Kisi, Ozgur and Ehteram, Mohammad and ELShafie, Ahmed and Torabi Haghighi, Ali (2021) Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models. Soft Computing, 25 (11). pp. 7609-7626. ISSN 1432-7643, DOI https://doi.org/10.1007/s00500-021-05721-5 <https://doi.org/10.1007/s00500-021-05721-5>. 10.1007/s00500-021-05721-5
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Darabi, Hamid
Mohamadi, Sedigheh
Karimidastenaei, Zahra
Kisi, Ozgur
Ehteram, Mohammad
ELShafie, Ahmed
Torabi Haghighi, Ali
Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models
description Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine-cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash-Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.
format Article
author Darabi, Hamid
Mohamadi, Sedigheh
Karimidastenaei, Zahra
Kisi, Ozgur
Ehteram, Mohammad
ELShafie, Ahmed
Torabi Haghighi, Ali
author_facet Darabi, Hamid
Mohamadi, Sedigheh
Karimidastenaei, Zahra
Kisi, Ozgur
Ehteram, Mohammad
ELShafie, Ahmed
Torabi Haghighi, Ali
author_sort Darabi, Hamid
title Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models
title_short Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models
title_full Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models
title_fullStr Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models
title_full_unstemmed Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models
title_sort prediction of daily suspended sediment load (ssl) using new optimization algorithms and soft computing models
publisher Springer
publishDate 2021
url http://eprints.um.edu.my/28830/
_version_ 1735409578436198400
score 13.211869

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