Computer Modeling of Oil Spill Trajectories With a High Accuracy Method

Garcia-Martinez, R.; Flores-Tovar, Henry

doi:10.1016/s1353-2561(99)00077-8

Cited by 31 publications

(4 citation statements)

References 6 publications

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“…In standard ocean trajectory prediction, the prediction of oil spills [5][6][7][8] and algal migration and diffusion belong to the particle prediction problem [9,10]. In particle prediction, numerical methods and models are often used to simulate particle motion.…”

Section: Previous Research Resultsmentioning

confidence: 99%

Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model

Yan,

Zhang,

Parvej

et al. 2023

Applied Sciences

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This paper proposes a novel Sea Drift Trajectory Prediction method based on the Quantum Convolutional Long Short-Term Memory (QCNN-LSTM) model. Accurately predicting sea drift trajectories is a challenging task, as they are influenced by various complex factors, such as ocean currents, wind speed, and wave morphology. Therefore, in a complex marine environment, there is a need for more applicable and computationally advanced prediction methods. Our approach combines quantized convolutional neural networks with Long Short-Term Memory networks, utilizing two different input types of prediction to enhance the network’s applicability. By incorporating quantization techniques, we improve the computational power and accuracy of the trajectory prediction. We evaluate our method using sea drift datasets and AUV drift trajectory datasets, comparing it with other commonly used traditional methods. The experimental results demonstrate significant improvements in accuracy and robustness achieved by our proposed Quantum Convolutional Long Short-Term Memory model. Regardless of the input mode employed, the accuracy consistently surpasses 98%. In conclusion, our research provides a new approach for sea drift trajectory prediction, enhancing prediction accuracy and providing valuable insights for marine environmental management and related decision-making. Future research can further explore and optimize this model to have a greater impact on marine prediction and applications.

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Section: Previous Research Resultsmentioning

confidence: 99%

Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model

Yan,

Zhang,

Parvej

et al. 2023

Applied Sciences

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“…Turbulent diffusive velocities u d are approximated as random fluctuations defined based on "random walk" (Garcia-Martinez and Flores-Tovar, 1999;Lonin, 1999;Isobe et al, 2009)…”

Section: Wind Drift Of Surfaced Oilmentioning

confidence: 99%

Development of the CSOMIO Coupled Ocean-Oil-Sediment- Biology Model

et al. 2021

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The fate and dispersal of oil in the ocean is dependent upon ocean dynamics, as well as transformations resulting from the interaction with the microbial community and suspended particles. These interaction processes are parameterized in many models limiting their ability to accurately simulate the fate and dispersal of oil for subsurface oil spill events. This paper presents a coupled ocean-oil-biology-sediment modeling system developed by the Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) project. A key objective of the CSOMIO project was to develop and evaluate a modeling framework for simulating oil in the marine environment, including its interaction with microbial food webs and sediments. The modeling system developed is based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport model (COAWST). Central to CSOMIO’s coupled modeling system is an oil plume model coupled to the hydrodynamic model (Regional Ocean Modeling System, ROMS). The oil plume model is based on a Lagrangian approach that describes the oil plume dynamics including advection and diffusion of individual Lagrangian elements, each representing a cluster of oil droplets. The chemical composition of oil is described in terms of three classes of compounds: saturates, aromatics, and heavy oil (resins and asphaltenes). The oil plume model simulates the rise of oil droplets based on ambient ocean flow and density fields, as well as the density and size of the oil droplets. The oil model also includes surface evaporation and surface wind drift. A novel component of the CSOMIO model is two-way Lagrangian-Eulerian mapping of the oil characteristics. This mapping is necessary for implementing interactions between the ocean-oil module and the Eulerian sediment and biogeochemical modules. The sediment module is a modification of the Community Sediment Transport Modeling System. The module simulates formation of oil-particle aggregates in the water column. The biogeochemical module simulates microbial communities adapted to the local environment and to elevated concentrations of oil components in the water column. The sediment and biogeochemical modules both reduce water column oil components. This paper provides an overview of the CSOMIO coupled modeling system components and demonstrates the capabilities of the modeling system in the test experiments.

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“…(2013)), to a more typical 4th-order Runge-Kutta method (see, e.g., García-Martínez and Flores-Tovar (1999)). Some alternatives seek to cut on computational time by using less evaluations, like the 4th-order Milne-predictor, Hamming-corrector integration scheme (see, e.g., Narváez et al (2012)), or the 4th-order Adams-Bashforth method (see, e.g., Yang et al (2008)).…”

Section: Introductionmentioning

confidence: 99%

Numerical integrators for Lagrangian oceanography

Nordam¹,

Duran²

2020

Preprint

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Abstract. A common task in Lagrangian oceanography is to calculate a large number of drifter trajectories from a velocity field pre-calculated with an ocean model. Mathematically, this is simply numerical integration of an Ordinary Differential Equation (ODE), for which a wide range of different methods exist. However, the discrete nature of the modelled ocean currents requires interpolation of the velocity field, and the choice of interpolation scheme has implications for the accuracy and efficiency of the different numerical ODE methods. We investigate trajectory calculation in modelled ocean currents with 800 m, 4 km, and 20 km horizontal resolution, in combination with linear, cubic and quintic spline interpolation. We use fixed-step Runge-Kutta integrators of orders 1–4, as well as three variable-step Runge-Kutta methods (Bogacki-Shampine 3(2), Dormand-Prince 5(4) and 8(7)). Additionally, we design and test modified special-purpose variants of the three variable-step integrators, that are better able to handle discontinuous derivatives in an interpolated velocity field. Our results show that the optimal choice of ODE integrator depends on the resolution of the ocean model, the degree of interpolation, and the desired accuracy. For cubic interpolation, the commonly used Dormand-Prince 5(4) is rarely the most efficient choice. We find that in many cases, our special-purpose integrators can improve accuracy by many orders of magnitude over their standard counterparts, with no increase in computational effort. The best results are seen for coarser resolutions (4 km and 20 km), thus the special-purpose integrators are particularly advantageous for research using regional to global ocean models to compute large numbers of trajectories. Our results are also applicable to trajectory computations from atmospheric models.

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Computer Modeling of Oil Spill Trajectories With a High Accuracy Method

Cited by 31 publications

References 6 publications

Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model

Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model

Development of the CSOMIO Coupled Ocean-Oil-Sediment- Biology Model

Numerical integrators for Lagrangian oceanography

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