Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions

Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological v...

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Main Authors: Ehteram, M., Ghotbi, S., Kisi, O., Ahmed, A.N., Hayder, G., Fai, C.M., Krishnan, M., Afan, H.A., EL-Shafie, A.
Format: Article
Language:English
Published: 2020
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Institution: Universiti Tenaga Nasional
Language: English
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spelling my.uniten.dspace-128322020-07-07T04:56:47Z Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions Ehteram, M. Ghotbi, S. Kisi, O. Ahmed, A.N. Hayder, G. Fai, C.M. Krishnan, M. Afan, H.A. EL-Shafie, A. Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL. © 2019 by the authors. 2020-02-03T03:27:08Z 2020-02-03T03:27:08Z 2019 Article 10.3390/app9194149 en
institution Universiti Tenaga Nasional
building UNITEN Library
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language English
description Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL. © 2019 by the authors.
format Article
author Ehteram, M.
Ghotbi, S.
Kisi, O.
Ahmed, A.N.
Hayder, G.
Fai, C.M.
Krishnan, M.
Afan, H.A.
EL-Shafie, A.
spellingShingle Ehteram, M.
Ghotbi, S.
Kisi, O.
Ahmed, A.N.
Hayder, G.
Fai, C.M.
Krishnan, M.
Afan, H.A.
EL-Shafie, A.
Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
author_facet Ehteram, M.
Ghotbi, S.
Kisi, O.
Ahmed, A.N.
Hayder, G.
Fai, C.M.
Krishnan, M.
Afan, H.A.
EL-Shafie, A.
author_sort Ehteram, M.
title Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
title_short Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
title_full Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
title_fullStr Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
title_full_unstemmed Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
title_sort investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
publishDate 2020
_version_ 1672614180795973632