Nabila Zrira , Assia Kamal-Idrissi , Rahma Farssi , Haris Ahmad Khan
{"title":"基于关注机制的 BiLSTM 模型的海面温度时间序列预测","authors":"Nabila Zrira , Assia Kamal-Idrissi , Rahma Farssi , Haris Ahmad Khan","doi":"10.1016/j.seares.2024.102472","DOIUrl":null,"url":null,"abstract":"<div><p>With the advancement of technology, ocean observation techniques have become increasingly prevalent in estimating marine variables such as Sea Surface Temperature (SST). This progress has led to a substantial surge in the volume of marine data. Presently, the abundance of available data presents a remarkable opportunity for training predictive models. The prediction of SST poses a challenge due to its temporal-dependent structure and multi-level seasonality. In this study, we propose a deep learning approach that combines the Bidirectional Long Short-Term Memory (BiLSTM) model with the attention mechanism to forecast SST. By leveraging the BiLSTM's ability to effectively capture long-term dependencies through both forward and backward LSTM processing, the attention mechanism accentuates salient features, thereby enhancing the model's evaluation accuracy.</p><p>To evaluate the effectiveness of the Attention-BiLSTM model in predicting SST, we conducted a case study in the Moroccan Sea, focusing on four distinct regions. We compared the performance of the Attention-BiLSTM model against alternative models such as LSTM, Attention-BiGRU, XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Transformers in forecasting the SST time series.</p><p>The experimental results unequivocally demonstrate that the Attention-BiLSTM model achieves significantly superior prediction outcomes and is a good candidate for deployment in the field.</p></div>","PeriodicalId":50056,"journal":{"name":"Journal of Sea Research","volume":"198 ","pages":"Article 102472"},"PeriodicalIF":2.1000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1385110124000054/pdfft?md5=8aee179f2b95f7a5abf32b2fb3532e3d&pid=1-s2.0-S1385110124000054-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Time series prediction of sea surface temperature based on BiLSTM model with attention mechanism\",\"authors\":\"Nabila Zrira , Assia Kamal-Idrissi , Rahma Farssi , Haris Ahmad Khan\",\"doi\":\"10.1016/j.seares.2024.102472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the advancement of technology, ocean observation techniques have become increasingly prevalent in estimating marine variables such as Sea Surface Temperature (SST). This progress has led to a substantial surge in the volume of marine data. Presently, the abundance of available data presents a remarkable opportunity for training predictive models. The prediction of SST poses a challenge due to its temporal-dependent structure and multi-level seasonality. In this study, we propose a deep learning approach that combines the Bidirectional Long Short-Term Memory (BiLSTM) model with the attention mechanism to forecast SST. By leveraging the BiLSTM's ability to effectively capture long-term dependencies through both forward and backward LSTM processing, the attention mechanism accentuates salient features, thereby enhancing the model's evaluation accuracy.</p><p>To evaluate the effectiveness of the Attention-BiLSTM model in predicting SST, we conducted a case study in the Moroccan Sea, focusing on four distinct regions. We compared the performance of the Attention-BiLSTM model against alternative models such as LSTM, Attention-BiGRU, XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Transformers in forecasting the SST time series.</p><p>The experimental results unequivocally demonstrate that the Attention-BiLSTM model achieves significantly superior prediction outcomes and is a good candidate for deployment in the field.</p></div>\",\"PeriodicalId\":50056,\"journal\":{\"name\":\"Journal of Sea Research\",\"volume\":\"198 \",\"pages\":\"Article 102472\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1385110124000054/pdfft?md5=8aee179f2b95f7a5abf32b2fb3532e3d&pid=1-s2.0-S1385110124000054-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Sea Research\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1385110124000054\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MARINE & FRESHWATER BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sea Research","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1385110124000054","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MARINE & FRESHWATER BIOLOGY","Score":null,"Total":0}
Time series prediction of sea surface temperature based on BiLSTM model with attention mechanism
With the advancement of technology, ocean observation techniques have become increasingly prevalent in estimating marine variables such as Sea Surface Temperature (SST). This progress has led to a substantial surge in the volume of marine data. Presently, the abundance of available data presents a remarkable opportunity for training predictive models. The prediction of SST poses a challenge due to its temporal-dependent structure and multi-level seasonality. In this study, we propose a deep learning approach that combines the Bidirectional Long Short-Term Memory (BiLSTM) model with the attention mechanism to forecast SST. By leveraging the BiLSTM's ability to effectively capture long-term dependencies through both forward and backward LSTM processing, the attention mechanism accentuates salient features, thereby enhancing the model's evaluation accuracy.
To evaluate the effectiveness of the Attention-BiLSTM model in predicting SST, we conducted a case study in the Moroccan Sea, focusing on four distinct regions. We compared the performance of the Attention-BiLSTM model against alternative models such as LSTM, Attention-BiGRU, XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Transformers in forecasting the SST time series.
The experimental results unequivocally demonstrate that the Attention-BiLSTM model achieves significantly superior prediction outcomes and is a good candidate for deployment in the field.
期刊介绍:
The Journal of Sea Research is an international and multidisciplinary periodical on marine research, with an emphasis on the functioning of marine ecosystems in coastal and shelf seas, including intertidal, estuarine and brackish environments. As several subdisciplines add to this aim, manuscripts are welcome from the fields of marine biology, marine chemistry, marine sedimentology and physical oceanography, provided they add to the understanding of ecosystem processes.