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Open AccessArticle

Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

by Wei Sun 1,2,*, Ying Lv 3, Gongchen Li 4 and Yumin Chen 4
1
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
3
MOE (Ministry of Education) Key Laboratory for Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
4
Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK S4S 0A2, Canada
*
Author to whom correspondence should be addressed.
Water 2020, 12(1), 220; https://doi.org/10.3390/w12010220
Received: 27 November 2019 / Revised: 24 December 2019 / Accepted: 7 January 2020 / Published: 13 January 2020
(This article belongs to the Section Water Resources Management and Governance)
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.
Keywords: river ice; breakup date; k-nearest neighbor; machine learning; ensemble learning river ice; breakup date; k-nearest neighbor; machine learning; ensemble learning
MDPI and ACS Style

Sun, W.; Lv, Y.; Li, G.; Chen, Y. Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble. Water 2020, 12, 220.

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