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A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia

comparative study; machine learning; river system; sensitivity analysis; suspended load; suspended sediment; water resource; Johor; Johor River; Krakatau; Lampung; Malaysia; Panjang; West Malaysia

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Main Authors: Hanoon M.S., Abdullatif B A.A., Ahmed A.N., Razzaq A., Birima A.H., El-Shafie A.
Other Authors: 57266877500
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-269602023-05-29T17:38:09Z A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia Hanoon M.S. Abdullatif B A.A. Ahmed A.N. Razzaq A. Birima A.H. El-Shafie A. 57266877500 57290875300 57214837520 57219410567 23466519000 16068189400 comparative study; machine learning; river system; sensitivity analysis; suspended load; suspended sediment; water resource; Johor; Johor River; Krakatau; Lampung; Malaysia; Panjang; West Malaysia Accurate and reliable suspended sediment load (SSL) prediction models are necessary for the planning and management of water resource structures. In this study, four machine learning techniques, namely Gradient boost regression (GBT), Random Forest (RF), Support vector machine (SVM), and Artificial neural network ANN will be developed to predict SSL at the Rantau Panjang station on Johor River basin (JRB), Malaysia. Four evaluation criteria, including the Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE) and Scatter Index (SI) will utilize to evaluating the performance of the proposed models. The obtained results revealed that all the proposed Machine Learning (ML) models showed superior prediction daily SSL performance. The comparative outcomes among models were carried out using the Taylor diagram. ANN model shows more reliable results than other models with R of 0.989, SI of 0.199, RMSE of 0.011053 and NSE of 0.979. A sensitivity analysis of the models to the input variables revealed that the absence of current day Suspended sediment load data SSLt-1 had the most effect on the SSL. Moreover, to examine validation of most accurate model we proposed divided data to 50% training, 25% testing and 25% validation) sets and ANN provided superior performance. Therefore, the proposed ANN approach is recommended as the most accurate model for SSL prediction. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:38:09Z 2023-05-29T09:38:09Z 2022 Article 10.1007/s12145-021-00689-0 2-s2.0-85116731881 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116731881&doi=10.1007%2fs12145-021-00689-0&partnerID=40&md5=0f6866dafa5bc236caa32b3df5700146 https://irepository.uniten.edu.my/handle/123456789/26960 15 1 91 104 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description comparative study; machine learning; river system; sensitivity analysis; suspended load; suspended sediment; water resource; Johor; Johor River; Krakatau; Lampung; Malaysia; Panjang; West Malaysia
author2 57266877500
author_facet 57266877500
Hanoon M.S.
Abdullatif B A.A.
Ahmed A.N.
Razzaq A.
Birima A.H.
El-Shafie A.
format Article
author Hanoon M.S.
Abdullatif B A.A.
Ahmed A.N.
Razzaq A.
Birima A.H.
El-Shafie A.
spellingShingle Hanoon M.S.
Abdullatif B A.A.
Ahmed A.N.
Razzaq A.
Birima A.H.
El-Shafie A.
A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia
author_sort Hanoon M.S.
title A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia
title_short A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia
title_full A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia
title_fullStr A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia
title_full_unstemmed A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia
title_sort comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in malaysia
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806427933697376256
score 13.222552

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