Evaluation of multi-source forcing datasets for drift trajectory prediction using Lagrangian models in the South China Sea

Zhang, Xuedong; Cheng, Liang; Zhang, Fangli; Wu, Jie; Li, Shuyi; Liu, Jiahui; Chu, Sensen; Xia, Nan; Min, Kaifu; Zuo, Xin; Li, Manchun

doi:10.1016/j.apor.2020.102395

Cited by 21 publications

(14 citation statements)

References 36 publications

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“…Zhang et al [2] suggested a probability model to estimate the target's drift velocity and position sequentially. Zhang et al [3] employed the Lagrange tracking algorithm and the Runge-Kutta method to validate the prediction of floating trajectories in the South China Sea using a variety of marine environmental data sets. Zhu et al [4] simulate the drift prediction process using the Runge-Kutta method and Lagrange tracking algorithm, then calculate the search area using the Monte Carlo approach.…”

Section: Introductionmentioning

confidence: 99%

Multi-target Emergency Rescue Path Planning in Marine Wind and Wave Environments

Zhi

Mei

et al. 2023

J. Phys.: Conf. Ser.

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With the development of marine-related industries, the number of tasks at sea has increased, and accidents there happen more frequently as well. Search and rescue are very challenging because of the extreme wind and wave environment at sea. Therefore, the state has been paying more attention to maritime emergency rescue. One part is emergency rescue path planning, which is crucial for helicopter emergency rescue at sea. In this paper, a series of multi-target rescue path solutions are created in order to solve the challenge of multi-target rescue path planning while taking into account sea wind and waves. The rescued targets’ floating trajectory model is first established. Then, a recursive algorithm based on a permutation tree is given in response to the unsteady change of the targets’ position at sea. The rescue path is planned with the goal of cutting down on the helicopter’s search time. Finally, using the multi-target rescue in China’s Taiwan Strait as an example, it is discovered that the overall time needed for the multi-target rescue is decreased by 7.03 minutes, and the efficiency of the rescue is improved by 13.3% in the wind and wave environment at sea. According to the research, the optimal rescue path shortens the time required for multi-target rescue at sea while increasing the effectiveness of helicopter rescue operations.

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Section: Introductionmentioning

confidence: 99%

Multi-target Emergency Rescue Path Planning in Marine Wind and Wave Environments

Zhi

Mei

et al. 2023

J. Phys.: Conf. Ser.

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“…However, uncertainties such as those caused by the coarse temporal and spatial resolution of the model, ocean waves, biofilms, wind forcing, water mixing, and tides can all affect the accuracy of float trajectory prediction (Declerck et al., 2019; Liu & Weisberg, 2011; Liu et al., 2014; Staneva et al., 2021). As a result, the separation error between the predicted and observed drifters, which is used to measure the prediction error, grows over time and can reach 10–25 km after a 1‐day prediction (Huntley et al., 2011; Liu & Weisberg, 2011; Zhang et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Improving Numerical Model Predicted Float Trajectories by Deep Learning

Shen

Bao

Pietrafesa

et al. 2022

Earth and Space Science

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The Lagrangian tracking approach is often used in numerical modeling (NM) to simulate and predict the movement of marine particles such as plastic, oil spills, and floating wreckage. The uncertainties in NM reduce prediction accuracy as a result of the coarse temporal and spatial resolution, along with waves, winds, and currents. From 29 August to 22 December 2020, the United States Defense Advanced Research Projects Agency's Ocean of Things program deployed 422 floating drifters in the Gulf of Mexico, providing an opportunity of using the observed trajectories as ground truth to train Artificial Intelligence (AI) models to correct NM float trajectory predictions. A Regional Ocean Model System (ROMS) and AI Hybrid model was developed to implement AI to correct the ROMS‐predicted 1‐day float trajectories. The AI model is built on a convolutional neural network and Gated Recurrent Unit. The results of the ROMS‐AI Hybrid model show that 82.0% of the trajectory predictions were improved at the 24 hr, with the corresponding overall mean separation error decreasing by 11.82 km, from 20.56 to 8.74 km, which is a 57% improvement, and the error growth rate decreasing from 5.06 km per 6 hr to 1.95 km per 6 hr. The evident improvement indicates that the ROMS‐AI Hybrid model can correct the ROMS simulation to improve the 1‐day prediction of the float trajectories and shows great potential to predict the cluster of the floats.

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“…FIG.54:(Color online) Sampled points in 5D parameter space, blue solid points are the sets which can reproduce two isospin diffusion data. Taken from Ref [460]…”

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confidence: 99%

“…55: (Color online) Stars are the isospin diffusion data at 35 MeV/nucleon and 50 MeV/nucleon[417,472], lines are the calculated isospin transport ratios with 120 parameter sets. Taken from Ref [460]…”

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confidence: 99%

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Progress of quantum molecular dynamics model and its applications in heavy ion collisions

Zhang

Wang

et al. 2020

Front. Phys.

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In this review article, we first briefly introduce the transport theory and quantum molecular dynamics model applied in the study of the heavy ion collisions from low to intermediate energies.The developments of improved quantum molecular dynamics model (ImQMD) and ultra-relativistic quantum molecular dynamics model (UrQMD), are reviewed. The reaction mechanism and phenomena related to the fusion, multinucleon transfer, fragmentation, collective flow and particle production are reviewed and discussed within the framework of the two models. The constraints on the isospin asymmetric nuclear equation of state and in-medium nucleon-nucleon cross sections by comparing the heavy ion collision data with transport models calculations in last decades are also discussed, and the uncertainties of these constraints are analyzed as well. Finally, we discuss the future direction of the development of the transport models for improving the understanding of the reaction mechanism, the descriptions of various observables, the constraint on the nuclear equation of state, as well as for the constraint on in-medium nucleon-nucleon cross sections.

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Evaluation of multi-source forcing datasets for drift trajectory prediction using Lagrangian models in the South China Sea

Cited by 21 publications

References 36 publications

Multi-target Emergency Rescue Path Planning in Marine Wind and Wave Environments

Multi-target Emergency Rescue Path Planning in Marine Wind and Wave Environments

Improving Numerical Model Predicted Float Trajectories by Deep Learning

Progress of quantum molecular dynamics model and its applications in heavy ion collisions

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