Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow

The intercomparison of streamflow simulation and the prediction of discharge using various renowned machine learning techniques were performed. The daily streamflow discharge model was developed for 35 observation stations located in a large-scale river basin named Cauvery. Various hydrological indi...

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Autores principales:	Parthiban Loganathan, Amit Baburao Mahindrakar
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	cauvery river basin climate change hydrological model machine learning streamflow Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/15d1237f55504149ba753cb71a61f845
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spelling	oai:doaj.org-article:15d1237f55504149ba753cb71a61f8452021-11-05T19:02:02ZIntercomparing the robustness of machine learning models in simulation and forecasting of streamflow2040-22442408-935410.2166/wcc.2020.365https://doaj.org/article/15d1237f55504149ba753cb71a61f8452021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/1824https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354The intercomparison of streamflow simulation and the prediction of discharge using various renowned machine learning techniques were performed. The daily streamflow discharge model was developed for 35 observation stations located in a large-scale river basin named Cauvery. Various hydrological indices were calculated for observed and predicted discharges for comparing and evaluating the replicability of local hydrological conditions. The model variance and bias observed from the proposed extreme gradient boosting decision tree model were less than 15%, which is compared with other machine learning techniques considered in this study. The model Nash–Sutcliffe efficiency and coefficient of determination values are above 0.7 for both the training and testing phases which demonstrate the effectiveness of model performance. The comparison of monthly observed and model-predicted discharges during the validation period illustrates the model's ability in representing the peaks and fall in high-, medium-, and low-flow zones. The assessment and comparison of hydrological indices between observed and predicted discharges illustrate the model's ability in representing the baseflow, high-spell, and low-spell statistics. Simulating streamflow and predicting discharge are essential for water resource planning and management, especially in large-scale river basins. The proposed machine learning technique demonstrates significant improvement in model efficiency by dropping variance and bias which, in turn, improves the replicability of local-scale hydrology. HIGHLIGHTS The credibility of machine learning models in representing the regional-scale hydrology is performed.; Evaluation to prioritize model selection for river basin management.; Season-based approach in evaluating model performance in local hydrology.; Hydrological indices were inter-compared for high-, medium-, and low-flow zones.; Outcome delivers valuable suggestions to decision-makers in the planning of future water resources.;Parthiban LoganathanAmit Baburao MahindrakarIWA Publishingarticlecauvery river basinclimate changehydrological modelmachine learningstreamflowEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 1824-1837 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cauvery river basin climate change hydrological model machine learning streamflow Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	cauvery river basin climate change hydrological model machine learning streamflow Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Parthiban Loganathan Amit Baburao Mahindrakar Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
description	The intercomparison of streamflow simulation and the prediction of discharge using various renowned machine learning techniques were performed. The daily streamflow discharge model was developed for 35 observation stations located in a large-scale river basin named Cauvery. Various hydrological indices were calculated for observed and predicted discharges for comparing and evaluating the replicability of local hydrological conditions. The model variance and bias observed from the proposed extreme gradient boosting decision tree model were less than 15%, which is compared with other machine learning techniques considered in this study. The model Nash–Sutcliffe efficiency and coefficient of determination values are above 0.7 for both the training and testing phases which demonstrate the effectiveness of model performance. The comparison of monthly observed and model-predicted discharges during the validation period illustrates the model's ability in representing the peaks and fall in high-, medium-, and low-flow zones. The assessment and comparison of hydrological indices between observed and predicted discharges illustrate the model's ability in representing the baseflow, high-spell, and low-spell statistics. Simulating streamflow and predicting discharge are essential for water resource planning and management, especially in large-scale river basins. The proposed machine learning technique demonstrates significant improvement in model efficiency by dropping variance and bias which, in turn, improves the replicability of local-scale hydrology. HIGHLIGHTS The credibility of machine learning models in representing the regional-scale hydrology is performed.; Evaluation to prioritize model selection for river basin management.; Season-based approach in evaluating model performance in local hydrology.; Hydrological indices were inter-compared for high-, medium-, and low-flow zones.; Outcome delivers valuable suggestions to decision-makers in the planning of future water resources.;
format	article
author	Parthiban Loganathan Amit Baburao Mahindrakar
author_facet	Parthiban Loganathan Amit Baburao Mahindrakar
author_sort	Parthiban Loganathan
title	Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
title_short	Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
title_full	Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
title_fullStr	Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
title_full_unstemmed	Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
title_sort	intercomparing the robustness of machine learning models in simulation and forecasting of streamflow
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/15d1237f55504149ba753cb71a61f845
work_keys_str_mv	AT parthibanloganathan intercomparingtherobustnessofmachinelearningmodelsinsimulationandforecastingofstreamflow AT amitbaburaomahindrakar intercomparingtherobustnessofmachinelearningmodelsinsimulationandforecastingofstreamflow
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Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow

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