Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf
The Arctic sea ice plays a significant role in climate-related processes and has a considerable effect on humans, however accurately predicting the Arctic sea ice concentration is still challenging. Recently, with the rise and development of artificial intelligence, big data technology, machine lear...
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ftfrontimediafig:oai:figshare.com:article/24434059 2024-09-15T18:34:05+00:00 Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf Yongcheng Lin Qinghua Yang Xuewei Li Chao-Yuan Yang Yiguo Wang Jiuke Wang Jingwen Liu Sizhe Chen Jiping Liu 2023-10-25T11:20:01Z https://doi.org/10.3389/fmars.2023.1260047.s001 https://figshare.com/articles/presentation/Presentation_1_Optimization_of_the_k-nearest-neighbors_model_for_summer_Arctic_Sea_ice_prediction_pdf/24434059 unknown doi:10.3389/fmars.2023.1260047.s001 https://figshare.com/articles/presentation/Presentation_1_Optimization_of_the_k-nearest-neighbors_model_for_summer_Arctic_Sea_ice_prediction_pdf/24434059 CC BY 4.0 Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea ice prediction summer Arctic machine learning KNN optimization Text Presentation 2023 ftfrontimediafig https://doi.org/10.3389/fmars.2023.1260047.s001 2024-08-19T06:20:03Z The Arctic sea ice plays a significant role in climate-related processes and has a considerable effect on humans, however accurately predicting the Arctic sea ice concentration is still challenging. Recently, with the rise and development of artificial intelligence, big data technology, machine learning has been widely used in the field of sea ice prediction. In this study, we utilized a sea ice concentration dataset obtained from satellite remote sensing and applied the k-nearest-neighbors (Ice-kNN) machine learning model to forecast the summer Arctic sea ice concentration and extent on 122 days prediction. Based on the physical characteristics of summer sea ice, different algorithms are employed to optimize the prediction model. A drift-ice correction algorithm is designed to address the unrealistic drift ice around the sea ice edge, and a distance function combined with the spatial pattern is proposed to enhance similarity detection. Deseasonalized and detrended sea ice datasets and an expanded training library are also utilized to improve model performance. Furthermore, sensitivity analysis reveals a positive impact of net surface heat flux on sea ice prediction. The modified Ice-kNN model outperforms climatological and anomaly persistence predictions, demonstrating its applicability to predicting summer Arctic sea ice. The September sea ice extent hindcasts of the modified Ice-kNN model are compared to a variety of models submitted to the Sea Ice Prediction Network, underscoring its potential to improve predictive skill for Arctic sea ice. Conference Object Sea ice Frontiers: Figshare |
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Open Polar |
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Frontiers: Figshare |
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ftfrontimediafig |
language |
unknown |
topic |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea ice prediction summer Arctic machine learning KNN optimization |
spellingShingle |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea ice prediction summer Arctic machine learning KNN optimization Yongcheng Lin Qinghua Yang Xuewei Li Chao-Yuan Yang Yiguo Wang Jiuke Wang Jingwen Liu Sizhe Chen Jiping Liu Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf |
topic_facet |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea ice prediction summer Arctic machine learning KNN optimization |
description |
The Arctic sea ice plays a significant role in climate-related processes and has a considerable effect on humans, however accurately predicting the Arctic sea ice concentration is still challenging. Recently, with the rise and development of artificial intelligence, big data technology, machine learning has been widely used in the field of sea ice prediction. In this study, we utilized a sea ice concentration dataset obtained from satellite remote sensing and applied the k-nearest-neighbors (Ice-kNN) machine learning model to forecast the summer Arctic sea ice concentration and extent on 122 days prediction. Based on the physical characteristics of summer sea ice, different algorithms are employed to optimize the prediction model. A drift-ice correction algorithm is designed to address the unrealistic drift ice around the sea ice edge, and a distance function combined with the spatial pattern is proposed to enhance similarity detection. Deseasonalized and detrended sea ice datasets and an expanded training library are also utilized to improve model performance. Furthermore, sensitivity analysis reveals a positive impact of net surface heat flux on sea ice prediction. The modified Ice-kNN model outperforms climatological and anomaly persistence predictions, demonstrating its applicability to predicting summer Arctic sea ice. The September sea ice extent hindcasts of the modified Ice-kNN model are compared to a variety of models submitted to the Sea Ice Prediction Network, underscoring its potential to improve predictive skill for Arctic sea ice. |
format |
Conference Object |
author |
Yongcheng Lin Qinghua Yang Xuewei Li Chao-Yuan Yang Yiguo Wang Jiuke Wang Jingwen Liu Sizhe Chen Jiping Liu |
author_facet |
Yongcheng Lin Qinghua Yang Xuewei Li Chao-Yuan Yang Yiguo Wang Jiuke Wang Jingwen Liu Sizhe Chen Jiping Liu |
author_sort |
Yongcheng Lin |
title |
Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf |
title_short |
Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf |
title_full |
Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf |
title_fullStr |
Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf |
title_full_unstemmed |
Presentation_1_Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction.pdf |
title_sort |
presentation_1_optimization of the k-nearest-neighbors model for summer arctic sea ice prediction.pdf |
publishDate |
2023 |
url |
https://doi.org/10.3389/fmars.2023.1260047.s001 https://figshare.com/articles/presentation/Presentation_1_Optimization_of_the_k-nearest-neighbors_model_for_summer_Arctic_Sea_ice_prediction_pdf/24434059 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
doi:10.3389/fmars.2023.1260047.s001 https://figshare.com/articles/presentation/Presentation_1_Optimization_of_the_k-nearest-neighbors_model_for_summer_Arctic_Sea_ice_prediction_pdf/24434059 |
op_rights |
CC BY 4.0 |
op_doi |
https://doi.org/10.3389/fmars.2023.1260047.s001 |
_version_ |
1810475807657689088 |