scholarly journals CranSLIK v2.0: improvements on the stochastic prediction of oil spill transport and fate using approximation methods

2015 ◽  
Vol 8 (6) ◽  
pp. 4949-4977
Author(s):  
R. Rutherford ◽  
I. Moulitsas ◽  
B. J. Snow ◽  
A. J. Kolios ◽  
M. De Dominicis
Keyword(s):  
Multiple Regression ◽  
Oil Spill ◽  
Stochastic Approach ◽  
Approximation Methods ◽  
Time Efficiency ◽  
Run Time

Abstract. Oil spill models are used to forecast the transport and fate of oil after it has been released. CranSLIK is a model that predicts the movement and spread of a surface oil spill at sea via a stochastic approach. The aim of this work is to identify parameters that can further improve the forecasting algorithms and expand the functionality of CranSLIK, while maintaining the run time efficiency of the method. The results from multiple simulations performed using the operational, validated oil spill model, MEDSLIK-II, were analysed using multiple regression in order to identify improvements which could be incorporated into CranSLIK. This has led to a revised model, namely CranSLIK v2.0, which was validated against MEDSLIK-II forecasts for real oil spill cases. The new version of CranSLIK demonstrated significant forecasting improvements by capturing the oil spill accurately in real validation cases and also proved capable of simulating a broader range of oil spill scenarios.

scholarly journals CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

2015 ◽  
Vol 8 (10) ◽  
pp. 3365-3377 ◽  
Author(s):  
R. Rutherford ◽  
I. Moulitsas ◽  
B. J. Snow ◽  
A. J. Kolios ◽  
M. De Dominicis
Keyword(s):  
Multiple Regression ◽  
Oil Spill ◽  
Stochastic Approach ◽  
Approximation Methods ◽  
Time Efficiency ◽  
Run Time

Abstract. Oil spill models are used to forecast the transport and fate of oil after it has been released. CranSLIK is a model that predicts the movement and spread of a surface oil spill at sea via a stochastic approach. The aim of this work is to identify parameters that can further improve the forecasting algorithms and expand the functionality of CranSLIK, while maintaining the run-time efficiency of the method. The results from multiple simulations performed using the operational, validated oil spill model, MEDSLIK-II, were analysed using multiple regression in order to identify improvements which could be incorporated into CranSLIK. This has led to a revised model, namely CranSLIK v2.0, which was validated against MEDSLIK-II forecasts for real oil spill cases. The new version of CranSLIK demonstrated significant forecasting improvements by capturing the oil spill accurately in real validation cases and also proved capable of simulating a broader range of oil spill scenarios.

scholarly journals CranSLIK v1.0: stochastic prediction of oil spill transport and fate using approximation methods

2014 ◽  
Vol 7 (4) ◽  
pp. 1507-1516 ◽  
Author(s):  
B. J. Snow ◽  
I. Moulitsas ◽  
A. J. Kolios ◽  
M. De Dominicis
Keyword(s):  
Oil Spill ◽  
Stochastic Approach ◽  
Water Velocity ◽  
Current Data ◽  
Point Mass ◽  
Approximation Methods ◽  
Centre Of Mass ◽  
Regression Modelling ◽  
Velocity Surface ◽  
Oceanographic Conditions

Abstract. This paper investigates the development of a model, called CranSLIK, to predict the transport and transformations of a point mass oil spill via a stochastic approach. Initially the various effects on destination are considered and key parameters are chosen which are expected to dominate the displacement. The variables considered are: wind velocity, surface water velocity, spill size, and spill age. For a point mass oil spill, it is found that the centre of mass can be determined by the wind and current data only, and the spill size and age can then be used to reconstruct the surface of the spill. These variables are sampled and simulations are performed using an open-source Lagrangian approach-based code, MEDSLIK II. Regression modelling is applied to create two sets of polynomials: one for the centre of mass, and one for the spill size. Simulations performed for a real oil spill case show that a minimum of approximately 80% of the oil is captured by CranSLIK. Finally, Monte Carlo simulation is implemented to allow for consideration of the most likely destination for the oil spill, when the distributions for the oceanographic conditions are known.

scholarly journals CranSLIK v1.0: stochastic prediction of oil spill transport and fate using approximation methods

2013 ◽  
Vol 6 (4) ◽  
pp. 7047-7076
Author(s):  
B. J. Snow ◽  
I. Moulitsas ◽  
A. J. Kolios ◽  
M. De Dominicis
Keyword(s):  
Oil Spill ◽  
Stochastic Approach ◽  
Water Velocity ◽  
Current Data ◽  
Point Mass ◽  
Approximation Methods ◽  
Centre Of Mass ◽  
Regression Modelling ◽  
Velocity Surface ◽  
Oceanographic Conditions

Abstract. This paper investigates the development of a model, called CranSLIK, to predict the transport and transformations of a point mass oil spill via a stochastic approach. Initially the various effects that affect the destination are considered and key parameters are chosen which are expected to dominate the displacement. The variables considered are: wind velocity, surface water velocity, spill size, and spill age. For a point mass oil spill, it is found that the centre of mass can be determined by the wind and current data only, and the spill size and age can then be used to reconstruct the surface of the spill. These variables are sampled and simulations are performed using an open-source Lagrangian approach-based code, MEDSLIK II. Regression modelling is applied to create two sets of polynomials: one for the centre of mass, and one for the spill size. A minimum of approximately 80% of the oil is captured for the Algeria scenario. Finally, Monte-Carlo simulation is implemented to allow for consideration of most likely destination for the oil spill, when the distributions for the oceanographic conditions are known.

How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?

2021 ◽  
Author(s):  
Michael Peters ◽  
Gian Luca Scoccia ◽  
Ivano Malavolta
Keyword(s):  
Time Efficiency ◽  
Android Apps ◽  
Run Time

scholarly journals Unfolding for CHR programs

2013 ◽  
Vol 15 (3) ◽  
pp. 264-311 ◽  
Author(s):  
MAURIZIO GABBRIELLI ◽  
MARIA CHIARA MEO ◽  
PAOLO TACCHELLA ◽  
HERBERT WIKLICKY
Keyword(s):  
Program Transformation ◽  
Large Body ◽  
Constraint Handling ◽  
Sequential Programs ◽  
Time Efficiency ◽  
Semantic Equivalence ◽  
Procedure Call ◽  
Run Time ◽  
Transformation Systems

AbstractProgram transformation is an appealing technique which allows to improve run-time efficiency, space-consumption, and more generally to optimize a given program. Essentially, it consists of a sequence of syntactic program manipulations which preserves some kind of semantic equivalence. Unfolding is one of the basic operations used by most program transformation systems and consists of the replacement of a procedure call by its definition. While there is a large body of literature on the transformation and unfolding of sequential programs, very few papers have addressed this issue for concurrent languages. This paper defines an unfolding system for Constraint Handling Rules programs. We define an unfolding rule, show its correctness and discuss some conditions that can be used to delete an unfolded rule while preserving the program meaning. We also prove that, under some suitable conditions, confluence and termination are preserved by the above transformation.

THE IMPORTANCE OF USING APPROPRIATE EXPERIMENTAL DESIGNS IN OIL SPILL IMPACT STUDIES: AN EXAMPLE FROM THE AMOCO CADIZ OIL SPILL IMPACT ZONE

1987 ◽  
Vol 1987 (1) ◽  
pp. 503-507
Author(s):  
Edward S. Gilfillan ◽  
David S. Page ◽  
Barbara Griffin ◽  
Sherry A. Hanson ◽  
Judith C. Foster
Keyword(s):  
Multiple Regression ◽  
Oil Spill ◽  
Aromatic Hydrocarbons ◽  
Body Burden ◽  
Condition Index ◽  
The Body ◽  
Coliform Bacteria ◽  
Stepwise Multiple Regression ◽  
Impact Zone ◽  
The North

ABSTRACT On March 16, 1978, the tanker Amoco Cadiz ran aground off the coast of North Brittany. Her cargo of 221,000 tons of light crude oil was released into the sea. More than 126 miles of coastline were oiled, including a number of oyster (Crassostrea gigas) growing establishments. The North Brittany coastline already was stressed by earlier additions of oil and metals. In December 1979, 21 months after the oil spill, measurements of glucose-6-phosphate dehydrogenase activity, aspartate aminotransferase activity, and condition index were made in 14 populations of C. gigas. Five of the populations were outside the oil spill impact zone; nine populations were within it. At the same time the body burden of Cr, Ag, Pd, Zn, Cu, Cd, and Hg, as well as the body burden of aliphatic and aromatic hydrocarbons, was determined. Also at the same time, the numbers of total and fecal coliform bacteria in the ambient water were determined. Results of stepwise multiple regression have shown that 21 months after the Amoco Cadiz oil spill, aromatic hydrocarbons were only one of three factors adversely affecting C. gigas populations in North Brittany. Multiple regression techniques can be extremely useful in identifying those stressors associated with physiological effects in populations of animals.

Sign in / Sign up

Export Citation Format

Share Document