Cover Image

Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new m...

Full description

Saved in:
Bibliographic Details
Main Authors: Muhammad Adnan, Rana, Yaseen, Zaher Mundher, Heddam, Salim, Shahid, Shamsuddin, Sadeghi-Niaraki, Aboalghasem, Kisi, Ozgur
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/101401/1/ShamsuddinShahid2022_PredictabilityPerformanceEnhancement.pdf
http://eprints.utm.my/id/eprint/101401/
http://dx.doi.org/10.1016/j.ijsrc.2021.10.001
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.101401
record_format eprints
spelling my.utm.1014012023-06-14T10:03:10Z http://eprints.utm.my/id/eprint/101401/ Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model Muhammad Adnan, Rana Yaseen, Zaher Mundher Heddam, Salim Shahid, Shamsuddin Sadeghi-Niaraki, Aboalghasem Kisi, Ozgur TA Engineering (General). Civil engineering (General) Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm (ANFIS-FCM-PSOGSA) in improving the estimation accuracy of river suspended sediment loads (SSLs) is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization (ANFIS-FCM-PSO), ANFIS-FCM, and sediment rating curve (SRC) models. Various input combinations involving lagged river flow (Q) and suspended sediment (S) values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), and coefficient of determination (R2) and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO (or ANFIS-FCM) models by 8.14% (1.72%), 14.7% (5.71%), 12.5% (2.27%), and 25.6% (1.86%), in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification. Elsevier B.V. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/101401/1/ShamsuddinShahid2022_PredictabilityPerformanceEnhancement.pdf Muhammad Adnan, Rana and Yaseen, Zaher Mundher and Heddam, Salim and Shahid, Shamsuddin and Sadeghi-Niaraki, Aboalghasem and Kisi, Ozgur (2022) Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model. International Journal of Sediment Research, 37 (3). pp. 383-398. ISSN 1001-6279 http://dx.doi.org/10.1016/j.ijsrc.2021.10.001 DOI: 10.1016/j.ijsrc.2021.10.001
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Muhammad Adnan, Rana
Yaseen, Zaher Mundher
Heddam, Salim
Shahid, Shamsuddin
Sadeghi-Niaraki, Aboalghasem
Kisi, Ozgur
Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model
description Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm (ANFIS-FCM-PSOGSA) in improving the estimation accuracy of river suspended sediment loads (SSLs) is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization (ANFIS-FCM-PSO), ANFIS-FCM, and sediment rating curve (SRC) models. Various input combinations involving lagged river flow (Q) and suspended sediment (S) values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), and coefficient of determination (R2) and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO (or ANFIS-FCM) models by 8.14% (1.72%), 14.7% (5.71%), 12.5% (2.27%), and 25.6% (1.86%), in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.
format Article
author Muhammad Adnan, Rana
Yaseen, Zaher Mundher
Heddam, Salim
Shahid, Shamsuddin
Sadeghi-Niaraki, Aboalghasem
Kisi, Ozgur
author_facet Muhammad Adnan, Rana
Yaseen, Zaher Mundher
Heddam, Salim
Shahid, Shamsuddin
Sadeghi-Niaraki, Aboalghasem
Kisi, Ozgur
author_sort Muhammad Adnan, Rana
title Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model
title_short Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model
title_full Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model
title_fullStr Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model
title_full_unstemmed Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy model
title_sort predictability performance enhancement for suspended sediment in rivers: inspection of newly developed hybrid adaptive neuro-fuzzy model
publisher Elsevier B.V.
publishDate 2022
url http://eprints.utm.my/id/eprint/101401/1/ShamsuddinShahid2022_PredictabilityPerformanceEnhancement.pdf
http://eprints.utm.my/id/eprint/101401/
http://dx.doi.org/10.1016/j.ijsrc.2021.10.001
_version_ 1769842049763246080
score 13.211869

Similar Items