Back to Search Start Over

Long Term Indian Ocean Dipole (IOD) Index Prediction Used Deep Learning by convLSTM

Authors :
Chen Li
Yuan Feng
Tianying Sun
Xingzhi Zhang
Source :
Remote Sensing, Vol 14, Iss 3, p 523 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Indian Ocean Dipole (IOD) is a large-scale physical ocean phenomenon in the Indian Ocean that plays an important role in predicting the El Nino Southern Oscillation in the tropical Pacific. Predicting the occurrence of IOD is of great significance to the study of climate change and other marine phenomena. Generally, the IOD index is calculated to judge whether the IOD occurs. In this paper, a convolutional LSTM (convLSTM) neural network is used to build the deep learning model to predict the sea surface temperature in the next seven months and calculate the IOD index. Through the analysis of marine atmospheric data with complex temporal and spatial relationships, the wind field signal knowledge of the physical ocean is introduced to predict IOD phenomenon by combining the prior knowledge of the physical ocean and deep learning. The experimental results show that the average correlation of IOD index time series to the true IOD index time series is 82.87% from 2015 to 2018, seven months ahead for IOD prediction. IOD manifests as sea surface temperature (SST) anomaly changes, and this thesis verifies that the wind field signal information has a positive impact on the prediction of IOD changes. Moreover, the convLSTM can predict this anomaly better. The IOD index line graph can generally fit the real IOD index variation trend, which has a profound impact on the study of the IOD phenomenon.

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.850ec374639b40847e0f23172dc
Document Type :
article
Full Text :
https://doi.org/10.3390/rs14030523