Effective ensemble learning approach for SST field prediction using attention-based PredRNN

Qiao, Bo; Wu, Zhongchen; Li, Ma; Zhou, Yifeng; Sun, Yunjiao

doi:10.1007/s11704-021-1080-7

Cited by 10 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, earth observation studies have also benefited from its success in enhancing model prediction accuracy. Qiao et al proposed an novel algorithm that combines an attention mechanism with recurrent neural networks to predict future seasurface temperature (SST) using historical SST data, and experimental results showed that it outperformed other SST prediction approaches [37]. Nevertheless, none of existing deeplearning-based algorithms employ attention over satellite passive microwave observations to exploit contexts among neighboring channels for improving accuracy of the hydrometeor classification task.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…Since then, its application has expanded tremendously over the past few decades [36]. Recently, convolutional neural networks (CNN) have emerged as one of the most popular deep-learning approaches to deep in various fields, including meteorological studies that involve remote sensing observations [37]. Because meteorological applications tend to have large earth observation datasets with spatially and temporally coherent information, conventional statistically based methodologies may not be accurate enough to capture the spatio-temporal patterns in the vast amount of earth observation data, especially for ice particles and snowflakes which are of non-spherical shape and, hence, are more sophisticated and have imperfectly known ice particle scattering properties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations

Chen

Bennartz

2023

Remote Sensing

View full text Add to dashboard Cite

This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development.

show abstract

Section: Attention Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations

Chen

Bennartz

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

Large language model ChatGPT versus small deep learning models for self‐admitted technical debt detection: Why not together?

Li,

Liu

et al. 2024

Softw Pract Exp

View full text Add to dashboard Cite

SummaryGiven the increasing complexity and volume of Self‐Admitted Technical Debts (SATDs), how to efficiently detect them becomes critical in software engineering practice for improving code quality and project efficiency. Although current deep learning methods have achieved good performance in detecting SATDs in code comments, they lack explanation. Large language models such as ChatGPT are increasingly being applied to text classification tasks due to their ability to provide explanations for classification results, but it is unclear how effective ChatGPT is for SATD classification. As the first in‐depth study of ChatGPT for SATD detection, we evaluate ChatGPT's effectiveness, compare it with small deep learning models, and find that ChatGPT performs better on Recall, while small models perform better on Precision. Furthermore, to enhance the performance of these approaches, we propose a novel fusion approach named FSATD which combines ChatGPT with small models for SATD detection so as to provide reliable explanations. Through extensive experiments on 62,276 comments from 10 open‐source projects, we show that FSATD outperforms existing methods in performance of F1‐score in cross‐project scenarios. Additionally, FSATD allows for flexible adjustment of fusion strategies, adapting to different requirements of various application scenarios, and can achieve the best Precision, Recall, or F1‐score.

show abstract

Comparison of Oil Field Production Prediction Methods Based on Machine Learning

Zhu

2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Effective ensemble learning approach for SST field prediction using attention-based PredRNN

Cited by 10 publications

References 17 publications

A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations

A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations

Large language model ChatGPT versus small deep learning models for self‐admitted technical debt detection: Why not together?

Comparison of Oil Field Production Prediction Methods Based on Machine Learning

Contact Info

Product

Resources

About