Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques

Rezaei, K; Pradhan, B; Vadiati, M; Nadiri, AA

Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques

Rezaei, K Pradhan, B

Vadiati, M Nadiri, AA

Permalink

Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Arabian Journal of Geosciences, 2021, 14, (3)
Issue Date:: 2021-02-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted manuscriptAdobe PDF (2.4 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Rezaei, K
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Vadiati, M
dc.contributor.author	Nadiri, AA
dc.date.accessioned	2021-05-05T00:33:51Z
dc.date.available	2021-05-05T00:33:51Z
dc.date.issued	2021-02-01
dc.identifier.citation	Arabian Journal of Geosciences, 2021, 14, (3)
dc.identifier.issn	1866-7511
dc.identifier.issn	1866-7538
dc.identifier.uri	http://hdl.handle.net/10453/148642
dc.description.abstract	Accurate prediction of suspended sediment (SS) concentration is a difficult task for water resource projects. In recent years, methodologies such as artificial intelligence (AI) algorithms have been applied for sediment load estimation and these models have provided efficient results. The present study investigates the abilities of four distinct AI approaches for estimating monthly SS load in Roodak station on Jajrood River, one of the longest waterways in the north of Iran, using the combinations of the present and antecedent monthly river flow data. This study aims to compare the predictive ability of artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), group method of data handling (GMDH), and least square support vector machines (LS-SVM) applied to predict the SS load. To develop the models, the monthly average river flow and the SS data for 50 years were obtained from Tehran regional water authority. Data were separated into three subsets (training, validation, and testing) and the SS concentration was predicted where the reliability of utilized approaches was assessed by statistical criterion including the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). A comparison of the developed models revealed that the use of antecedent average river flow is able to enhance the prediction precision of suspended sediment concentration. The results indicate that the LS-SVM model generated superior results than the other models in terms of the mean error criteria, showing the ability of the model to reasonably predict the observed SS load values.
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Arabian Journal of Geosciences
dc.relation.isbasedon	10.1007/s12517-020-06408-1
dc.rights	The final publication is available at Springer via http://dx.doi.org/ 10.1007/s12517-020-06408-1
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0403 Geology
dc.title	Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0403 Geology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-05T00:33:49Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	3

Abstract:

Accurate prediction of suspended sediment (SS) concentration is a difficult task for water resource projects. In recent years, methodologies such as artificial intelligence (AI) algorithms have been applied for sediment load estimation and these models have provided efficient results. The present study investigates the abilities of four distinct AI approaches for estimating monthly SS load in Roodak station on Jajrood River, one of the longest waterways in the north of Iran, using the combinations of the present and antecedent monthly river flow data. This study aims to compare the predictive ability of artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), group method of data handling (GMDH), and least square support vector machines (LS-SVM) applied to predict the SS load. To develop the models, the monthly average river flow and the SS data for 50 years were obtained from Tehran regional water authority. Data were separated into three subsets (training, validation, and testing) and the SS concentration was predicted where the reliability of utilized approaches was assessed by statistical criterion including the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). A comparison of the developed models revealed that the use of antecedent average river flow is able to enhance the prediction precision of suspended sediment concentration. The results indicate that the LS-SVM model generated superior results than the other models in terms of the mean error criteria, showing the ability of the model to reasonably predict the observed SS load values.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148642