Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble
Forecasting of river ice breakup timing is directly related to the local ice-caused flooding management. However, river ice forecasting using k-nearest neighbor (kNN) algorithms is limited. Thus, a kNN stacking ensemble learning (KSEL) method was developed and applied to forecasting breakup dates (B...
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ftdoajarticles:oai:doaj.org/article:2d6a81da88dd4c338d3f126108747bae 2023-05-15T15:26:04+02:00 Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble Wei Sun Ying Lv Gongchen Li Yumin Chen 2020-01-01T00:00:00Z https://doi.org/10.3390/w12010220 https://doaj.org/article/2d6a81da88dd4c338d3f126108747bae EN eng MDPI AG https://www.mdpi.com/2073-4441/12/1/220 https://doaj.org/toc/2073-4441 2073-4441 doi:10.3390/w12010220 https://doaj.org/article/2d6a81da88dd4c338d3f126108747bae Water, Vol 12, Iss 1, p 220 (2020) river ice breakup date k-nearest neighbor machine learning ensemble learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 article 2020 ftdoajarticles https://doi.org/10.3390/w12010220 2022-12-30T22:15:44Z Forecasting of river ice breakup timing is directly related to the local ice-caused flooding management. However, river ice forecasting using k-nearest neighbor (kNN) algorithms is limited. Thus, a kNN stacking ensemble learning (KSEL) method was developed and applied to forecasting breakup dates (BDs) for the Athabasca River at Fort McMurray in Canada. The kNN base models with diverse inputs and distance functions were developed and their outputs were further combined. The performance of these models was examined using the leave-one-out cross validation method based on the historical BDs and corresponding climate and river conditions in 1980−2015. The results indicated that the kNN with the Chebychev distance functions generally outperformed other kNN base models. Through the simple average methods, the ensemble kNN models using multiple-type (Mahalanobis and Chebychev) distance functions had the overall optimal performance among all models. The improved performance indicates that the kNN ensemble is a promising tool for river ice forecasting. The structure of optimal models also implies that the breakup timing is mainly linked with temperature and water flow conditions before breakup as well as during and just after freeze up. Article in Journal/Newspaper Athabasca River Fort McMurray Directory of Open Access Journals: DOAJ Articles Fort McMurray Athabasca River Canada Water 12 1 220 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
river ice breakup date k-nearest neighbor machine learning ensemble learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
spellingShingle |
river ice breakup date k-nearest neighbor machine learning ensemble learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Wei Sun Ying Lv Gongchen Li Yumin Chen Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble |
topic_facet |
river ice breakup date k-nearest neighbor machine learning ensemble learning Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
description |
Forecasting of river ice breakup timing is directly related to the local ice-caused flooding management. However, river ice forecasting using k-nearest neighbor (kNN) algorithms is limited. Thus, a kNN stacking ensemble learning (KSEL) method was developed and applied to forecasting breakup dates (BDs) for the Athabasca River at Fort McMurray in Canada. The kNN base models with diverse inputs and distance functions were developed and their outputs were further combined. The performance of these models was examined using the leave-one-out cross validation method based on the historical BDs and corresponding climate and river conditions in 1980−2015. The results indicated that the kNN with the Chebychev distance functions generally outperformed other kNN base models. Through the simple average methods, the ensemble kNN models using multiple-type (Mahalanobis and Chebychev) distance functions had the overall optimal performance among all models. The improved performance indicates that the kNN ensemble is a promising tool for river ice forecasting. The structure of optimal models also implies that the breakup timing is mainly linked with temperature and water flow conditions before breakup as well as during and just after freeze up. |
format |
Article in Journal/Newspaper |
author |
Wei Sun Ying Lv Gongchen Li Yumin Chen |
author_facet |
Wei Sun Ying Lv Gongchen Li Yumin Chen |
author_sort |
Wei Sun |
title |
Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble |
title_short |
Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble |
title_full |
Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble |
title_fullStr |
Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble |
title_full_unstemmed |
Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble |
title_sort |
modeling river ice breakup dates by k-nearest neighbor ensemble |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/w12010220 https://doaj.org/article/2d6a81da88dd4c338d3f126108747bae |
geographic |
Fort McMurray Athabasca River Canada |
geographic_facet |
Fort McMurray Athabasca River Canada |
genre |
Athabasca River Fort McMurray |
genre_facet |
Athabasca River Fort McMurray |
op_source |
Water, Vol 12, Iss 1, p 220 (2020) |
op_relation |
https://www.mdpi.com/2073-4441/12/1/220 https://doaj.org/toc/2073-4441 2073-4441 doi:10.3390/w12010220 https://doaj.org/article/2d6a81da88dd4c338d3f126108747bae |
op_doi |
https://doi.org/10.3390/w12010220 |
container_title |
Water |
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12 |
container_issue |
1 |
container_start_page |
220 |
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1766356633357647872 |