Staff View: Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting

Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting

Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support v...

Full description

Saved in:

Bibliographic Details
Main Authors:	Adib, M. N. M., Harun, Sobri
Format:	Article
Published:	American Society of Civil Engineers (ASCE) 2022
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/103255/ http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0002176
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utm.103255
record_format	eprints
spelling	my.utm.1032552023-10-24T10:03:39Z http://eprints.utm.my/103255/ Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting Adib, M. N. M. Harun, Sobri TA Engineering (General). Civil engineering (General) Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling-Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976-1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995-2005). Future projections of multi-GCM over the 2021-2080 period under three SSPs predicted an increase in rainfall for all months except April-June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051-2080 period relative to the baseline period of 1976-2005. The model predicted that the seasonal changes in streamflow of two planting periods range between -7.5% and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release. American Society of Civil Engineers (ASCE) 2022 Article PeerReviewed Adib, M. N. M. and Harun, Sobri (2022) Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting. American Society of Civil Engineers (ASCE), 27 (6). n/a. ISSN 1084-0699 http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0002176 DOI: 10.1061/(ASCE)HE.1943-5584.0002176
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Adib, M. N. M. Harun, Sobri Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
description	Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling-Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976-1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995-2005). Future projections of multi-GCM over the 2021-2080 period under three SSPs predicted an increase in rainfall for all months except April-June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051-2080 period relative to the baseline period of 1976-2005. The model predicted that the seasonal changes in streamflow of two planting periods range between -7.5% and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release.
format	Article
author	Adib, M. N. M. Harun, Sobri
author_facet	Adib, M. N. M. Harun, Sobri
author_sort	Adib, M. N. M.
title	Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
title_short	Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
title_full	Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
title_fullStr	Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
title_full_unstemmed	Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
title_sort	metalearning approach coupled with cmip6 multi-gcm for future monthly streamflow forecasting
publisher	American Society of Civil Engineers (ASCE)
publishDate	2022
url	http://eprints.utm.my/103255/ http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0002176
_version_	1781777669211291648
score	13.211869

Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting

Similar Items