Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea
Accurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indi...
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ftdoajarticles:oai:doaj.org/article:93d0931f12be44e7a358790b467cb0b4 2023-05-15T17:36:25+02:00 Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea Heechan Han Donghyun Kim Wonjoon Wang Hung Soo Kim 2023-02-01T00:00:00Z https://doi.org/10.2166/ws.2023.012 https://doaj.org/article/93d0931f12be44e7a358790b467cb0b4 EN eng IWA Publishing http://ws.iwaponline.com/content/23/2/934 https://doaj.org/toc/1606-9749 https://doaj.org/toc/1607-0798 1606-9749 1607-0798 doi:10.2166/ws.2023.012 https://doaj.org/article/93d0931f12be44e7a358790b467cb0b4 Water Supply, Vol 23, Iss 2, Pp 934-947 (2023) deep learning algorithm large-scale climate variability monthly dam inflow prediction Water supply for domestic and industrial purposes TD201-500 River lake and water-supply engineering (General) TC401-506 article 2023 ftdoajarticles https://doi.org/10.2166/ws.2023.012 2023-04-09T00:31:09Z Accurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indices, Atlantic multidecadal oscillation (AMO), El Niño–southern oscillations (ENSO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), Niño 3.4, and Southern Oscillation Index (SOI) for the period of 1981–2020, were used as input variables of the model. The proposed model was trained with 29 years of data (1981–2009) and tested with 12 years of data (2009–2020). We investigated 29 input data combinations to evaluate the predictive performance according to different input datasets. The model showed the average values of metrics ranged from 0.5 to 0.6 for CC and from 40 to 80 cm for root mean square error (RMSE) at three dams. The prediction results from the model showed lower performance as the lead time increased. Also, each dam showed different prediction results for different seasons. For example, Soyangriver/Daecheong dams have better accuracy in prediction for the wet season than the dry season, whereas the Andong dam has a high prediction ability during the dry season. These investigations can be used for better efficient dam management using a data-driven approach. HIGHLIGHTS A dam inflow prediction model was developed using the LSTM-based deep learning method and climate indices.; Six climate indices including AMO, ENSO, NAO, PDO, Niño 3.4, and SOI are considered as input variables.; The proposed model tests 29 different input combinations to find the best combinations for prediction.; The proposed model shows the applicability to predict dam inflow variability in different locations.; Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Pacific Soi ENVELOPE(30.704,30.704,66.481,66.481) Water Supply 23 2 934 947 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
deep learning algorithm large-scale climate variability monthly dam inflow prediction Water supply for domestic and industrial purposes TD201-500 River lake and water-supply engineering (General) TC401-506 |
spellingShingle |
deep learning algorithm large-scale climate variability monthly dam inflow prediction Water supply for domestic and industrial purposes TD201-500 River lake and water-supply engineering (General) TC401-506 Heechan Han Donghyun Kim Wonjoon Wang Hung Soo Kim Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea |
topic_facet |
deep learning algorithm large-scale climate variability monthly dam inflow prediction Water supply for domestic and industrial purposes TD201-500 River lake and water-supply engineering (General) TC401-506 |
description |
Accurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indices, Atlantic multidecadal oscillation (AMO), El Niño–southern oscillations (ENSO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), Niño 3.4, and Southern Oscillation Index (SOI) for the period of 1981–2020, were used as input variables of the model. The proposed model was trained with 29 years of data (1981–2009) and tested with 12 years of data (2009–2020). We investigated 29 input data combinations to evaluate the predictive performance according to different input datasets. The model showed the average values of metrics ranged from 0.5 to 0.6 for CC and from 40 to 80 cm for root mean square error (RMSE) at three dams. The prediction results from the model showed lower performance as the lead time increased. Also, each dam showed different prediction results for different seasons. For example, Soyangriver/Daecheong dams have better accuracy in prediction for the wet season than the dry season, whereas the Andong dam has a high prediction ability during the dry season. These investigations can be used for better efficient dam management using a data-driven approach. HIGHLIGHTS A dam inflow prediction model was developed using the LSTM-based deep learning method and climate indices.; Six climate indices including AMO, ENSO, NAO, PDO, Niño 3.4, and SOI are considered as input variables.; The proposed model tests 29 different input combinations to find the best combinations for prediction.; The proposed model shows the applicability to predict dam inflow variability in different locations.; |
format |
Article in Journal/Newspaper |
author |
Heechan Han Donghyun Kim Wonjoon Wang Hung Soo Kim |
author_facet |
Heechan Han Donghyun Kim Wonjoon Wang Hung Soo Kim |
author_sort |
Heechan Han |
title |
Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea |
title_short |
Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea |
title_full |
Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea |
title_fullStr |
Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea |
title_full_unstemmed |
Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea |
title_sort |
dam inflow prediction using large-scale climate variability and deep learning approach: a case study in south korea |
publisher |
IWA Publishing |
publishDate |
2023 |
url |
https://doi.org/10.2166/ws.2023.012 https://doaj.org/article/93d0931f12be44e7a358790b467cb0b4 |
long_lat |
ENVELOPE(30.704,30.704,66.481,66.481) |
geographic |
Pacific Soi |
geographic_facet |
Pacific Soi |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Water Supply, Vol 23, Iss 2, Pp 934-947 (2023) |
op_relation |
http://ws.iwaponline.com/content/23/2/934 https://doaj.org/toc/1606-9749 https://doaj.org/toc/1607-0798 1606-9749 1607-0798 doi:10.2166/ws.2023.012 https://doaj.org/article/93d0931f12be44e7a358790b467cb0b4 |
op_doi |
https://doi.org/10.2166/ws.2023.012 |
container_title |
Water Supply |
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23 |
container_issue |
2 |
container_start_page |
934 |
op_container_end_page |
947 |
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1766135888479256576 |