Mixture Density Networks for Tropical Cyclone Tracks Prediction in South China Sea

Hao, Fengyun; Li, Dou; Jin, Jian

doi:10.1109/ijcnn52387.2021.9534422

Cited by 2 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning-based TC track models have been developed [12] for TC simulations. 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].…”

Section: A Indexesmentioning

confidence: 99%

Value of Resilience for Self-Sustained Highway Transportation Systems

Liu

Yan

Zeng

et al. 2024

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

Section: A Indexesmentioning

confidence: 99%

Value of Resilience for Self-Sustained Highway Transportation Systems

Liu

Yan

Zeng

et al. 2024

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

“…To construct the feature space for the track prediction model, the latitude, longitude, and central pressure of each record were extracted from the raw data. These values were then used to calculate the predictors selected by the climatology and persistence (CLIPER) models (Knaff et al, 2003;Hao et al, 2021). Two sets of predictors are calculated for the latitude and longitude prediction, respectively.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…However, this method does not consider uncertainty along the track. Mixture Density Networks (MDN) are better for modeling TC track uncertainties as they directly learn the parameter distributions (Dabrowski et al, 2020;Hao et al, 2021). In this work, we adopt MDN similarly to MLR-based statistical models, splitting the track prediction into deterministic and random error tasks, learned by a fully connected NN and MDN respectively.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic track model for tropical cyclone risk assessment using multitask learning

Jian,

Liu,

Zhao

2023

Front. Energy Res.

View full text Add to dashboard Cite

Tropical cyclone (TC) track forecasting is critical for wind risk assessment. This work proposes a novel probabilistic TC track forecasting model based on mixture density network (MDN) and multitask learning (MTL). The existing NN-based probabilistic TC track prediction models focus on directly modeling the distribution of the future TC positions. Multitask learning has been shown to boost the performance of single tasks when the tasks are relevant. This work divides the probabilistic track prediction task into two sub-tasks: a deterministic prediction of the future TC position and a probabilistic prediction of the residual between the deterministic prediction and the actual TC location. The MDN is employed to realize the probabilistic prediction task. Since the target values of the MDN in this work are the residuals, which depend on the prediction result of the deterministic task, a novel training method is developed to train the MTL model properly. The proposed model is tested against statistical and other learning-based models on historical TC data. The results show that the proposed model outperforms other models in making probabilistic predictions. This approach advances TC track forecasting by integrating MDN and MTL, showing promise in enhancing probabilistic predictions and improving disaster preparedness.

show abstract

Mixture Density Networks for Tropical Cyclone Tracks Prediction in South China Sea

Cited by 2 publications

References 17 publications

Value of Resilience for Self-Sustained Highway Transportation Systems

Value of Resilience for Self-Sustained Highway Transportation Systems

A probabilistic track model for tropical cyclone risk assessment using multitask learning

Contact Info

Product

Resources

About