A typhoon trajectory prediction model based on multimodal and multitask learning

Qin, Wanting; Tang, Jun; Lu, Cong; Lao, Songyang

doi:10.1016/j.asoc.2022.108804

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…Meanwhile, selecting appropriate kernel functions for shared information and private information can more effectively deal with different information, which makes our proposed model have strong robust performance. For the fair, the traditional algorithms can achieve a better learning effect on the small-scale problems, while deep learning models have better advantages in dealing with large-scale data mining problems [32]. Therefore, how to effectively integrate multitask learning and deep learning to solve the real-world scenarios is also an attractive issue.…”

Section: Discussionmentioning

confidence: 99%

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Zhang

et al. 2022

Axioms

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Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. Theoretical analysis shows that these models can be converted to linear systems. A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. We tested the proposed models by applying them to forecasts of the Chinese stock market index trend and the stock prices of five stated-owned banks. The experimental results demonstrate their validity.

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Section: Discussionmentioning

confidence: 99%

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Zhang

et al. 2022

Axioms

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“…Later, by applying the multimodal data based on typhoon track data and satellite images, Ref. [116] integrated the LSTM and 3D CNN model to predict typhoon trajectory. In spite of widespread RNN structures, Ref.…”

Section: Rnn-based Tracking Methodsmentioning

confidence: 99%

“…In addition, Figure 4 shows a comparison of algorithm structure between two categories. [110] 2021 GAN with deep multi-scale frame prediction method [111] 2022 GAN to predict both the track and intensity of typhoons RNN-based [112] 2017 A convolutional sequence-to-sequence autoencoder [113] 2018 MNNs for typhoon tracking [114] 2018 A CLSTM based model [115] 2021 A CLSTM layer with FCLs [116] 2022 A CLSTM with 3D CNN based on multimodal data [117] 2022 An echo state network-based tracking Fire Traditional [118] 2017 Identify possible fire hotspots from two bands of AHI [119] 2018 A threshold algorithm with visual interpretation [120] 2019 A multi-temporal method of temperature estimation [121] 2020 Temperature dynamics by data assimilation [122] 2022 Wildfire tracking via visible and infrared image series DL-based [123] 2019 3D CNN to capture spatial and spectral patterns [124] 2019 Inception-v3 model with transfer learning [125] 2021 Near-real-time fire smoking prediction [126] 2022 Combine the residual convolution and separable convolution to detect fire [127] 2022 Multiple Kernel learning for various size fire detections…”

Section: Ship Trackingmentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.

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“…Combined with mixture models, deep learning models are able to make conditional probabilistic predictions of TC tracks and intensities [13]. Deep learning models, once properly trained, generally perform better than statistical models [14]. However, deep learning models are extremely sensitive to hyperparameters that are hard to tune manually.…”

Section: A Indexesmentioning

confidence: 99%