Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Abstract Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalizat...

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Autores principales:	Pakorn Ditthakit, Sirimon Pinthong, Nureehan Salaeh, Fadilah Binnui, Laksanara Khwanchum, Quoc Bao Pham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/59617bb324cc47ca801293a763e2af9b
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spelling	oai:doaj.org-article:59617bb324cc47ca801293a763e2af9b2021-12-02T18:37:08ZUsing machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin10.1038/s41598-021-99164-52045-2322https://doaj.org/article/59617bb324cc47ca801293a763e2af9b2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99164-5https://doaj.org/toc/2045-2322Abstract Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.Pakorn DitthakitSirimon PinthongNureehan SalaehFadilah BinnuiLaksanara KhwanchumQuoc Bao PhamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pakorn Ditthakit Sirimon Pinthong Nureehan Salaeh Fadilah Binnui Laksanara Khwanchum Quoc Bao Pham Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin
description	Abstract Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.
format	article
author	Pakorn Ditthakit Sirimon Pinthong Nureehan Salaeh Fadilah Binnui Laksanara Khwanchum Quoc Bao Pham
author_facet	Pakorn Ditthakit Sirimon Pinthong Nureehan Salaeh Fadilah Binnui Laksanara Khwanchum Quoc Bao Pham
author_sort	Pakorn Ditthakit
title	Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin
title_short	Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin
title_full	Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin
title_fullStr	Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin
title_full_unstemmed	Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin
title_sort	using machine learning methods for supporting gr2m model in runoff estimation in an ungauged basin
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/59617bb324cc47ca801293a763e2af9b
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Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

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