Research on PDO index prediction based on multivariate LSTM neural network model

Yu Zhenlong; Xu Dongfeng; Yao Zhixiong; Yang Chenghao; Liu Songnan

doi:10.12284/hyxb2022047

Volume 44 Issue 6

Jul. 2022

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Yu Zhenlong，Xu Dongfeng，Yao Zhixiong, et al. Research on PDO index prediction based on multivariate LSTM neural network model[J]. Haiyang Xuebao，2022, 44(6)：58–67 doi: 10.12284/hyxb2022047

Citation:

Yu Zhenlong，Xu Dongfeng，Yao Zhixiong, et al. Research on PDO index prediction based on multivariate LSTM neural network model[J]. Haiyang Xuebao，2022, 44(6)：58–67 doi: 10.12284/hyxb2022047

Citation:

Yu Zhenlong，Xu Dongfeng，Yao Zhixiong, et al. Research on PDO index prediction based on multivariate LSTM neural network model[J]. Haiyang Xuebao，2022, 44(6)：58–67 doi: 10.12284/hyxb2022047

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Research on PDO index prediction based on multivariate LSTM neural network model

doi: 10.12284/hyxb2022047

Yu Zhenlong^1
,,
Xu Dongfeng^{1, 2
,
,},
Yao Zhixiong^{1, 2},
Yang Chenghao^{1, 2},
Liu Songnan¹

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

Received Date: 2021-06-02
Rev Recd Date: 2021-08-03

Available Online: 2022-07-13

Publish Date: 2022-07-13

Abstract

Abstract

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

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References(39)

References

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