Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being...

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Autores principales:	B. M. Sreedhara, Amit Prakash Patil, Jagalingam Pushparaj, Geetha Kuntoji, Sujay Raghavendra Naganna
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	clear-water scour gmdh gradient tree boosting live-bed scour scour prediction Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/139e6543694e4a8797bc80ace3adbe0b
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id	oai:doaj.org-article:139e6543694e4a8797bc80ace3adbe0b
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spelling	oai:doaj.org-article:139e6543694e4a8797bc80ace3adbe0b2021-11-05T17:49:04ZApplication of gradient tree boosting regressor for the prediction of scour depth around bridge piers1464-71411465-173410.2166/hydro.2021.011https://doaj.org/article/139e6543694e4a8797bc80ace3adbe0b2021-07-01T00:00:00Zhttp://jh.iwaponline.com/content/23/4/849https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes. HIGHLIGHTS An attempt has been made to predict the bridge scour for various pier shapes under different streambed conditions.; Ensemble Machine Learning methods like Gradient Tree Boosting (GTB) and Group Method of Data Handling (GMDH) were used for prediction of bridge scour.; The study concludes that GTB method can be successfully used for predicting bridge scour.;B. M. SreedharaAmit Prakash PatilJagalingam PushparajGeetha KuntojiSujay Raghavendra NagannaIWA Publishingarticleclear-water scourgmdhgradient tree boostinglive-bed scourscour predictionInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 4, Pp 849-863 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clear-water scour gmdh gradient tree boosting live-bed scour scour prediction Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	clear-water scour gmdh gradient tree boosting live-bed scour scour prediction Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 B. M. Sreedhara Amit Prakash Patil Jagalingam Pushparaj Geetha Kuntoji Sujay Raghavendra Naganna Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
description	Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes. HIGHLIGHTS An attempt has been made to predict the bridge scour for various pier shapes under different streambed conditions.; Ensemble Machine Learning methods like Gradient Tree Boosting (GTB) and Group Method of Data Handling (GMDH) were used for prediction of bridge scour.; The study concludes that GTB method can be successfully used for predicting bridge scour.;
format	article
author	B. M. Sreedhara Amit Prakash Patil Jagalingam Pushparaj Geetha Kuntoji Sujay Raghavendra Naganna
author_facet	B. M. Sreedhara Amit Prakash Patil Jagalingam Pushparaj Geetha Kuntoji Sujay Raghavendra Naganna
author_sort	B. M. Sreedhara
title	Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
title_short	Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
title_full	Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
title_fullStr	Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
title_full_unstemmed	Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
title_sort	application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/139e6543694e4a8797bc80ace3adbe0b
work_keys_str_mv	AT bmsreedhara applicationofgradienttreeboostingregressorforthepredictionofscourdeptharoundbridgepiers AT amitprakashpatil applicationofgradienttreeboostingregressorforthepredictionofscourdeptharoundbridgepiers AT jagalingampushparaj applicationofgradienttreeboostingregressorforthepredictionofscourdeptharoundbridgepiers AT geethakuntoji applicationofgradienttreeboostingregressorforthepredictionofscourdeptharoundbridgepiers AT sujayraghavendranaganna applicationofgradienttreeboostingregressorforthepredictionofscourdeptharoundbridgepiers
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Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

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