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Research on PDO index prediction based on multivariate LSTM neural network model
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Zhenlong Yu1, Dongfeng Xu1, 2, *, Zhixiong Yao1, 2, Chenghao Yang1, 2, Songnan Liu1
Haiyang Xuebao | 2022, 44(6) : 58 - 67
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Haiyang Xuebao | 2022, 44(6): 58-67
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Research on PDO index prediction based on multivariate LSTM neural network model
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Zhenlong Yu1, Dongfeng Xu1, 2, *, Zhixiong Yao1, 2, Chenghao Yang1, 2, Songnan Liu1
Affiliations
  • 1. State Key Laboratory of Satellite Ocean Environmental Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • 2. Zhejiang Institute of Marine Sciences, Hangzhou 310012, China
  • 3. State Key Laboratory of Marine Space Resource Management Technology, Ministry of Natural Resources, Hangzhou 310012, China
Published: 2022-05-25 doi: 10.12284/hyxb2022047
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A multivariate long short term memory (LSTM) neural network model was developed for the Pacific decadal oscillation (PDO) index time series prediction using sea level pressure, sea level height, ocean heat content data and sea ice concentration from 1921 to 2020 as forecast elements of the PDO index. The PDO index prediction results of different time series from 2011 to 2020 were compared and analyzed, and finally the PDO index forecasting from 2021 to 2030 is realized by using the multivariate LSTM neural network model. The results show that the average correlation coefficient and root mean square error of the predicted value and the observed value of the multivariate LSTM model after cross-validation are 0.70 and 0.62, respectively. PDO will remain in the cold phase in the next ten years, and the PDO index will fluctuate twice, there will be a minimum in 2025. Compared with other time series forecasting models, the multivariate LSTM neural network model used in this paper has less error in forecasting results and good fitting effect, which can be used as a new method of predicting PDO index.

PDO index  /  LSTM neural network model  /  time series forecasting
Zhenlong Yu, Dongfeng Xu, Zhixiong Yao, Chenghao Yang, Songnan Liu. Research on PDO index prediction based on multivariate LSTM neural network model[J]. Haiyang Xuebao, 2022 , 44 (6) : 58 -67 . DOI: 10.12284/hyxb2022047
Year 2022 volume 44 Issue 6
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Article Info
doi: 10.12284/hyxb2022047
  • Receive Date:2021-06-02
  • Online Date:2026-02-01
  • Published:2022-05-25
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  • Received:2021-06-02
  • Revised:2021-08-03
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Affiliations
    1. State Key Laboratory of Satellite Ocean Environmental Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
    2. Zhejiang Institute of Marine Sciences, Hangzhou 310012, China
    3. State Key Laboratory of Marine Space Resource Management Technology, Ministry of Natural Resources, Hangzhou 310012, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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