Comparative study for deriving stagedischarge - sediment concentration relationships using soft computing techniques

• Autorzy: Sihag P. , Sadikhani M. R. , Vambol V. , Vambol S. , Prabhakar A. K. , Sharma N.

Identyfikatory

DOI

10.5604/01.3001.0014.8489

• Języki publikacji: EN

Abstrakty

EN

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Słowa kluczowe

EN

sediment load concentration; Baitarani river; M5P; random forest

PL

ładunek osadu; stężenie; rzeka Baitarani; las losowy

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2021
• Tom: Vol. 104, nr 2
• Strony: 57--76
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Sihag P.

• Civil engineering Department, Shoolini University, Solan, Himachal Pradesh, India

• https://orcid.org/0000-0002-7761-0603

autor

Sadikhani M. R.

• Soil science Department, College of Agriculture, Lorestan University, Iran

autor

Vambol V.

• Public Agency "National Scientific and Research Institute of Industrial Safety and Occupational Safety and Health", Kyiv, Ukraine

• https://orcid.org/0000-0002-8229-3956

autor

Vambol S.

• Kharkiv Petro Vasylenko National Technical University of Agriculture, Kharkiv, Ukraine

• https://orcid.org/0000-0002-8376-9020

autor

Prabhakar A. K.

• Civil Engineering Department, National Institute of Kurukshetra, Haryana, India

autor

Sharma N.

• Civil engineering Department, Shoolini University, Solan, Himachal Pradesh, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).