Here you can find different simulation models developed using some of our tools, or from scratch.

The simulation of a snow slap avalanche phenomena using GISSim techniques for the information representation and EVOSim techniques for the propagation.

In the next figure a representation of an avalanche using the simulation model is shown.

The validation and the verification of the model is performed thanks the information provided by the IGC.

There are 2 thesis related with this project here.

If you have a question about the publications please feel free to ask me anything.

## GISRUK 2007
11-13th April 2007. NUI Maynooth, (Ireland) |

## Computers, Environment and Urban Systems 2010
http://www.elsevier.com/wps/find/journaldescription.cws_home/304/description#description 23 August 2010 |

Embedded Scribd iPaper - Requires Javascript and Flash Player

Using GIS data in a m:nACk cellular automata to perform an avalanche

GISRUK 2007

Index

• • • • • • Avalanche problem Cellular automata m:n-ACk Avalanche model Results Conclusions

GISRUK 2007

2

Avalanche

Two main types of snow avalanche: • Loose-snow avalanche originates at a point and propagates downhill by successively dislodging increasing numbers of poorly cohering snow grains, typically gaining width as movement continues down slope. • Slab avalanche, occurs when a distinct cohesive snow layer breaks away as a unit and slides because it is poorly Cellular automata – the snow or ground Avalanche – anchored to m:n-Ack – Avalanche model – Results

– Conclusions

GISRUK 2007 3

Avalanche fatalities in IKAR Countries

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 4

Cellular automata

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 5

Game of life

• The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular automaton. • Glider gun and

GISRUK 2007 6

m:n-ACk

A multi n dimensional cellular automaton is a cellular automaton generalization composed by m layers with n dimensions each one. The representation is: • m:n-ACk Where • m: is the automaton number of layers. • n: is the different layers dimension. • Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results k: is the number of main layers (1 by

– Conclusions

GISRUK 2007 7

m:n-ACk

• Defined over the mathematical topology concept. • 1:n-AC 1 is the common cellular automata if the topology used is the discrete topology defined over N or Z. • The implementation, as is usual, can be a matrix.

GISRUK 2007 8

State of the automata

• Em[x1,..,xn], layer m state in x1,..,xn position

– Em is a function describing cell state in position x1,..,xn of layer m.

• EG[x1,..,xn], automata status in x1,..,xn position.

– EG returns automata global state in position georeferenced by coordinates x1,..,xn. Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results

– Conclusions

GISRUK 2007 9

Evolution Function m

• Function defined for the layer m to modify its state through the state of others layers using combination function Ψ, and vicinity and nucleus functions.

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 10

Avalanche Model data

GISRUK 2007

11

Avalanche Model

• 6+N:2-AC4+N on Z2

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 12

Vicinity and nucleus function

• Vicinity function: vn(x1,x1) = {(x11,x2-1), (x1-1,x2), (x1-1,x2+1), (x1,x2-1), (x1,x2), (x1,x2+1), (x1+1,x2-1), (x1+1,x2), (x1+1,x2+1)} • Nucleus function: nc(x1,x1)= {(x1,x1)}

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 13

Evolution functions

• E2[i]: Thickness of the snow. The function that rules this layer is “Modify information(p)” • E4[i]: Density, compactness of the snow, in our case is 0.5 (Mears 1976). • E6[i]: State of the snow. The function is defined in the next diagrams. • EN[i]: Obstacles. The function that

GISRUK 2007

14

SDL formalism

• Object-oriented, formal language defined by The International Telecommunications Union as recommendation Z.100. • Intended for the specification of complex, event-driven, real-time, and interactive applications involving many concurrent activities that communicate using discrete signals.

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 15

Moore neighbourhood

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 16

Λ:state of the snow

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 17

Empty process

GISRUK 2007

18

Results

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 19

Results

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 20

Conclusions

• An application to represent in virtual reality format the avalanche phenomena using GIS data thought the m:n-CAk cellular automaton is presented. • The comparison of the output data with studied phenomena shows promising results. • The structure in layers simplify the calculus of the evolution functions, allowing an easier implementation of the model, and a clear specification. • Avalanche –represents all m:n-Ack – Avalanche model – Results Layers Cellular automata – the model variables,

– Conclusions

GISRUK 2007 21

Thanks!

Pau Fonseca i Casas pau@fib.upc.edu

Technical University of Catalonia Barcelona School of Informatics Computing laboratory Barcelona Jordi Girona 1-3 (+34)93401773

GISRUK 2007 22

Static process

GISRUK 2007

23

Dynamic process

GISRUK 2007

24

Evolution function

• The increment in the force is used in the next expression to determine if the snow continues its movement to other cell, or stops its movement, if the force is equal to zero.

GISRUK 2007

25

Evolution function

• Where • IFi,t=Impulse force, depends on the quantity and quality of the snow, and the slope. • SFFi,t= Sliding friction force between the avalanche and the underlying snow or ground. • IFFi,t= Internal dynamic shear resistance due to collisions and momentum exchange between particles and blocks of snow, (internal friction force). • ASFFi,t= Turbulent friction within the snow/air suspension, (air suspension friction force). • AFFi,t=Shear between the avalanche and the surrounding air, (air friction force). • FFFi,t= Fluid-dynamic drag at the front of the avalanche

GISRUK 2007 26

5

GISRUK 2007

27

5

GISRUK 2007

28

5

GISRUK 2007

29

1 vs 3

GISRUK 2007

30

Using GIS data in a m:nACk cellular automata to perform an avalanche

GISRUK 2007

Index

• • • • • • Avalanche problem Cellular automata m:n-ACk Avalanche model Results Conclusions

GISRUK 2007

2

Avalanche

Two main types of snow avalanche: • Loose-snow avalanche originates at a point and propagates downhill by successively dislodging increasing numbers of poorly cohering snow grains, typically gaining width as movement continues down slope. • Slab avalanche, occurs when a distinct cohesive snow layer breaks away as a unit and slides because it is poorly Cellular automata – the snow or ground Avalanche – anchored to m:n-Ack – Avalanche model – Results

– Conclusions

GISRUK 2007 3

Avalanche fatalities in IKAR Countries

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 4

Cellular automata

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 5

Game of life

• The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular automaton. • Glider gun and

GISRUK 2007 6

m:n-ACk

A multi n dimensional cellular automaton is a cellular automaton generalization composed by m layers with n dimensions each one. The representation is: • m:n-ACk Where • m: is the automaton number of layers. • n: is the different layers dimension. • Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results k: is the number of main layers (1 by

– Conclusions

GISRUK 2007 7

m:n-ACk

• Defined over the mathematical topology concept. • 1:n-AC 1 is the common cellular automata if the topology used is the discrete topology defined over N or Z. • The implementation, as is usual, can be a matrix.

GISRUK 2007 8

State of the automata

• Em[x1,..,xn], layer m state in x1,..,xn position

– Em is a function describing cell state in position x1,..,xn of layer m.

• EG[x1,..,xn], automata status in x1,..,xn position.

– EG returns automata global state in position georeferenced by coordinates x1,..,xn. Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results

– Conclusions

GISRUK 2007 9

Evolution Function m

• Function defined for the layer m to modify its state through the state of others layers using combination function Ψ, and vicinity and nucleus functions.

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 10

Avalanche Model data

GISRUK 2007

11

Avalanche Model

• 6+N:2-AC4+N on Z2

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 12

Vicinity and nucleus function

• Vicinity function: vn(x1,x1) = {(x11,x2-1), (x1-1,x2), (x1-1,x2+1), (x1,x2-1), (x1,x2), (x1,x2+1), (x1+1,x2-1), (x1+1,x2), (x1+1,x2+1)} • Nucleus function: nc(x1,x1)= {(x1,x1)}

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 13

Evolution functions

• E2[i]: Thickness of the snow. The function that rules this layer is “Modify information(p)” • E4[i]: Density, compactness of the snow, in our case is 0.5 (Mears 1976). • E6[i]: State of the snow. The function is defined in the next diagrams. • EN[i]: Obstacles. The function that

GISRUK 2007

14

SDL formalism

• Object-oriented, formal language defined by The International Telecommunications Union as recommendation Z.100. • Intended for the specification of complex, event-driven, real-time, and interactive applications involving many concurrent activities that communicate using discrete signals.

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 15

Moore neighbourhood

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 16

Λ:state of the snow

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 17

Empty process

GISRUK 2007

18

Results

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 19

Results

Avalanche – Cellular automata – m:n-Ack – Avalanche model – Results – Conclusions GISRUK 2007 20

Conclusions

• An application to represent in virtual reality format the avalanche phenomena using GIS data thought the m:n-CAk cellular automaton is presented. • The comparison of the output data with studied phenomena shows promising results. • The structure in layers simplify the calculus of the evolution functions, allowing an easier implementation of the model, and a clear specification. • Avalanche –represents all m:n-Ack – Avalanche model – Results Layers Cellular automata – the model variables,

– Conclusions

GISRUK 2007 21

Thanks!

Pau Fonseca i Casas pau@fib.upc.edu

Technical University of Catalonia Barcelona School of Informatics Computing laboratory Barcelona Jordi Girona 1-3 (+34)93401773

GISRUK 2007 22

Static process

GISRUK 2007

23

Dynamic process

GISRUK 2007

24

Evolution function

• The increment in the force is used in the next expression to determine if the snow continues its movement to other cell, or stops its movement, if the force is equal to zero.

GISRUK 2007

25

Evolution function

• Where • IFi,t=Impulse force, depends on the quantity and quality of the snow, and the slope. • SFFi,t= Sliding friction force between the avalanche and the underlying snow or ground. • IFFi,t= Internal dynamic shear resistance due to collisions and momentum exchange between particles and blocks of snow, (internal friction force). • ASFFi,t= Turbulent friction within the snow/air suspension, (air suspension friction force). • AFFi,t=Shear between the avalanche and the surrounding air, (air friction force). • FFFi,t= Fluid-dynamic drag at the front of the avalanche

GISRUK 2007 26

5

GISRUK 2007

27

5

GISRUK 2007

28

5

GISRUK 2007

29

1 vs 3

GISRUK 2007

30

The simulation of a wildfire using the BEHAVE model and GISSim techniques for the propagation. Some EVOSim techniques are used to represent the extinction model. (video).

All the elements can be defined using VRML or X3D languaje. The simulator uses a default library with the more common elements needed to repesent the simulation model.

In the next picture, a landscape is shown. This is the initial view of a model, representing the diferent layers that configures the variables. Usually a first layer with the DTM (Digital Terrain Model) is used as a base for all the other layers (vegetation, paths, rivers, etc). Since the model can define different types ofvegetation, different layers representing each one of these layers can be used in the simulation.

Once the model is configured, the simulation can be executed. A least one point is selected, as the origin of the fire. The simulation engine calculates the fire evolution representing the wildfire as can be viewed in the last picture.

This video shows the execution of a wildfire model.

Once the simulation finish the results can be represented in the virtual reality environment.

The virtual reality model is build using VRML with the Cortona browser embedded in the application.

## GISRUK 2006
http://www.nottingham.ac.uk/geography/GISRUK/ 5-7h April 2006, University of Nottingham (UK) |

## GISRUK 2004
http://www.geo.ed.ac.uk/gisruk/gisruk.html 28-30h April 2004, University of East Anglia (Norwich) (UK) |

Three different researches I conduct regarding water.

One is related with the flow of the water in a defined environment, representing the river basins and other elements.

The other research is related with the sea level rise in two different aspects, the representation, and the construction of a model to obtain the new level of the water. In the media section two videos showing the preliminary models are shown.

The last research is related with the propagation of a black tide. In the figure is shown a propagation of petroleum over the sea surface using VRML language to represent the model.

Here you can see the sea level rise model in the city of New York. The model elevates the water aproximately 100 meters (its not real, here we are testing the web browser infraestructure to represent the simulation model). | |

The same model in the city of Barcelona. |

If you have a question about the publications please feel free to ask me anything.

## ASM 2008
June 23 – 25, 2008 Corfú (Grece) |

Embedded Scribd iPaper - Requires Javascript and Flash Player

SDL FORMALIZATION OF A HYDROLOGIC MODEL

Jeaneth López C., Pau Fonseca i Casas, Josep Casanovas

Outline

§ § § § § § Hydrologic Models Simulation Formalisms GIS data Cellular automaton SDL Specification of the model. Discussion

Hydrologic Models

IPCC estimates that there are four places in the earth with high risk to reduce the availability of hydrologic resources and present abrupt climate changes. Spain (south of Europe), California (EEUU), South Africa, North east of

Greenhouse effects

Hydrologic resource

Rise Sea level Natural disastres

•Cyclones •Droughts •Floods •Frost •Etc…

Human Migration

Rise Temperature

Water Availability

Increase of population Precipi tation Contami nation Waste water

Human Consume Agricul ture

Available Water

Industry Ecosystems

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

Watershed modeling, or hydrologic model (rainfall-runoff modeling) began in the 1950s and 1960s with the evolution of the digital computer. The Stanford Watershed Model (SWM) was one of the first watershed hydrologic model. (Crawford and Linsey 1966). LSSMs (Large-scale Spatial Models)(Watson et al., 1999) DHSVM (Distributed Hydrology-Soil-Vegetation Model) (Wigmosta 1997, 2002) LSHWM (Large-Scale Hybrid Watershed Modeling)(Aral and Gunduz, 2003), MHM (Macroscale Hydrological Model using Variable Infiltration Capacity VIC 3-layer)(Srinivasan2003) The VIC-3L model, is based on a 3-layer Soil Vegetation Atmosphere Transfer scheme to model different surface conditions. L-THIA (Long Term Hydrologic Impacts Assessment) (Lim et al., 2006) Hydrological model SHALL3 (Zimmermann, 2002) (Haan et al.,1982, Singh, 1988, Singh and Frevert, 2006)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

The SWAT (Soil and Water Assessment Tool) was developed by modifying the models specified below. All of this models use the SCS Curve Number Method, an empirical formula for predicting runoff from daily rainfall.

GLEAMS

pesticide compone nt daily rainfall hydrology component Crop Growth compone nt

QUAL2 E

Instream kinetics

Example SWAT adaptations

ESWAT SWATG SWIM

SWRRB

(multiple subbasins, subbasins, other other

CREAMS

SWRRB (multiple

SWAT

routing structur e

components)

EPIC

ROTO

Schematic of SWAT developmental history, including selected SWAT

1970s

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Nowada ys

Simulation Formalisms

§ Petri Nets

§ Introduced by Carl Adam Petri (1960). Also known as a Place/Transition

§ DEVS (Discreet Event System Specification)

§ In the 70s Bernard Zeigler proposed a mathematical formalism.

§ SDL (Specification and Description Language)

§ The semantics of SDL was defined formally in 1988.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Petri Nets

RdP=(P,T,A,W,M0)

P = Finiteset of nodes Places T = Finiteset of nodes Transitions A C(PxT) and T. (TxP): subset of cartesianproduct between nodes P

W = a {1,2,3,..}: weigh of arcs M0 = Pi {1,2,3,..} node Pi: number ok tokens initials in each place

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

DEVS Formalism

M=<X, S, Y, δint, δext, λ, ta> where: X: set of input values. S: set of states values. Y: set of output values. δint: internal transition function.

δint :S

S

δext: external transition function. δext Q x X S , on

Q={(s,e)|sS, 0≤e≤ta(s)} set of states. e elapsed time from the last transition.

λ : output function: λ : S Y ta : time advance function.

ta: S

R+0∞

DEVS Processor example

M=<X, S, Y, δint, δext, λ, ta> on: X={job1, job2, .., jobn} S={job1, job2,.., jobn}U[Ø]xR+0∞ Y={y(job1), y(job2),.. Y(jobn)} δint(job,σ)=(Ø, ∞) δext(job,σ,e,x)= if job= Ø (x,tp,(x)) else (job, σ-e) λ(job, σ)=y(job) Ta(job, σ)= σ

SDL formalism

Object-oriented, formal language defined by The International Telecommunications Union as recommendation Z.100. Intended for the specification of complex, event-driven, real-time, and interactive applications involving many concurrent activities that communicate using discrete signals.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

SDL Formalism

The semantics of SDL was defined formally in 1988.

Standard Formal Graphical

and symbol-based Object-oriented (OO)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

GIS Data

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Celular automaton

Each cell xi(t) represent a state of the cell i in the instant t. k = {Empty, Static and Dynamic}

xi

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Game of life

The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Method

§ Cellular Automaton Structure

§ Evolution Rules

§ Precipitation phase § Settlement phase

• Data layers

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Soil (Ground)

•Return Flow •Kind of soil •Saturate •Kind of soil use •Non-Saturate •Percolation

•Runoff •Humidity •Evapo-trans piration •Temperature •Rainfall •Precipitation

Hydrologic resource

Meteorology

Factors/variables which affects hydrologic resource

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Where:

SWt is the total quantity of water in the soil (basin) (mm), SWi is the initial quantity of water in the soil (mm) Ri (rainfall) is the quantity of rain in the period of analysis(mm) Qi is the quantity of runoff (mm) ETi is the evapotranspiration (mm) Pi is the percolation (mm) QRi return flow(mm) t is the time expressed in days i is the index of cell

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

This expression gives us the water volume existing in each cell of our cellular automaton in a specific time t. In the expression, one position of the cellular automaton matrix [xi,xj] is represented by the cell xi.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

System diagram

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

States diagram

Update Receive

Send

Dynamic

Receiv e

Static

d en S te/ ei ec R ve

Update Receive

Receive

Send

Up

da

Send Update

Empty

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Static state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Empty state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Dynamic state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Discussion and future work

This slides depicts the first and more important step to construct a discrete hydrologic simulator based in a cellular automaton. It shows the specification of hydrologic model with the aim of simulate the behaviour of water in a river basin. The use of the cellular simplifies the mathematical expressions and helps us to maintain a structured view of the geographical data needed to represent the model. The use of SDL formalism simplifies the understanding of the behaviour of model thanks its graphical structure. The model is based in the hydrologic balance equation which permits calculate the quantity exist in each cell of the cellular automaton

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

References

[1] L. Ma, L.R. Ahuja, and R.W. Malone. Systems Modeling for Soil and Water Research and Management: Current Status and Needs for the 21st Century. ASABE, 1907-2007. [2] IPCC Intergovernamental Panel on Climate Change. Report IPCC. 2007. [3] J. Samper, M.A. Garcia, B. Pisani, D. Alvares, A. Varela, and J.A. Losada. Modelos Hidrologicos y Sistemas de Informacion Geografica para la Estimacion de Recursos Hidricos:. Confederacin Hidrologica del Ebro, VII, 2005. [4] E. Guzman, J. Bonini, and D. Matamoros. Modelo Hidrologico SWAT (Soil Water Assessment Tool) Prediccin de Caudales y Sedimentos en una Cuenca. Revista Tecnologica, 17:152–161, 2004. [5] Erik D. Zimmermann. Modelo Hidrologico Superficial y Subterraneo. Centro Universitario rosario de Investigaciones Hidroambientales. [6] A. López, D. Sauri, and Jose M. Galán. Urban Water

References

[8] P. Pea and J. Alirio. Sobre las redes de petri r-difusas. Divulgaciones Matemticas, 7:87–99, 1999. [9] Gabriel Wainer. Applying cell-devs methodology for modeling the environment. SIMULATION, 82:635, 2006. [10] Liu Qi and Gabriel Wainer. Parallel environment for devs and cell-devs models. SIMULATION, 83:449, 2007. [11] Fonseca P. and Casanovas J. 2005. ESS205, Simplifying GIS data use inside discrete event simulation model through m_n-AC cellular automaton; Proceedings ESS 2005. [12] ITU-T Telecommunication Standardization Sector of ITU. Languages and general software aspects for telecommunication systems. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU, 2007. [13] Zeigler, B. P.; H. Praehofer; D. Kim. 2000. Theory of Modeling and Simulation. Academic Press. [14] Petri, Carl A. 1962. "Kommunikation mit Automaten". Ph. D. Thesis. University of Bonn. [15] Recalde, L.; Teruel, E.; Silva, M. 1999. Autonomous

Thanks!

Pau Fonseca i Casas pau@fib.upc.edu

Technical University of Catalonia Barcelona School of Informatics Computing laboratory Barcelona Jordi Girona 1-3 (+34)93401773

SDL FORMALIZATION OF A HYDROLOGIC MODEL

Jeaneth López C., Pau Fonseca i Casas, Josep Casanovas

Outline

§ § § § § § Hydrologic Models Simulation Formalisms GIS data Cellular automaton SDL Specification of the model. Discussion

Hydrologic Models

IPCC estimates that there are four places in the earth with high risk to reduce the availability of hydrologic resources and present abrupt climate changes. Spain (south of Europe), California (EEUU), South Africa, North east of

Greenhouse effects

Hydrologic resource

Rise Sea level Natural disastres

•Cyclones •Droughts •Floods •Frost •Etc…

Human Migration

Rise Temperature

Water Availability

Increase of population Precipi tation Contami nation Waste water

Human Consume Agricul ture

Available Water

Industry Ecosystems

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

Watershed modeling, or hydrologic model (rainfall-runoff modeling) began in the 1950s and 1960s with the evolution of the digital computer. The Stanford Watershed Model (SWM) was one of the first watershed hydrologic model. (Crawford and Linsey 1966). LSSMs (Large-scale Spatial Models)(Watson et al., 1999) DHSVM (Distributed Hydrology-Soil-Vegetation Model) (Wigmosta 1997, 2002) LSHWM (Large-Scale Hybrid Watershed Modeling)(Aral and Gunduz, 2003), MHM (Macroscale Hydrological Model using Variable Infiltration Capacity VIC 3-layer)(Srinivasan2003) The VIC-3L model, is based on a 3-layer Soil Vegetation Atmosphere Transfer scheme to model different surface conditions. L-THIA (Long Term Hydrologic Impacts Assessment) (Lim et al., 2006) Hydrological model SHALL3 (Zimmermann, 2002) (Haan et al.,1982, Singh, 1988, Singh and Frevert, 2006)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

The SWAT (Soil and Water Assessment Tool) was developed by modifying the models specified below. All of this models use the SCS Curve Number Method, an empirical formula for predicting runoff from daily rainfall.

GLEAMS

pesticide compone nt daily rainfall hydrology component Crop Growth compone nt

QUAL2 E

Instream kinetics

Example SWAT adaptations

ESWAT SWATG SWIM

SWRRB

(multiple subbasins, subbasins, other other

CREAMS

SWRRB (multiple

SWAT

routing structur e

components)

EPIC

ROTO

Schematic of SWAT developmental history, including selected SWAT

1970s

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Nowada ys

Simulation Formalisms

§ Petri Nets

§ Introduced by Carl Adam Petri (1960). Also known as a Place/Transition

§ DEVS (Discreet Event System Specification)

§ In the 70s Bernard Zeigler proposed a mathematical formalism.

§ SDL (Specification and Description Language)

§ The semantics of SDL was defined formally in 1988.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Petri Nets

RdP=(P,T,A,W,M0)

P = Finiteset of nodes Places T = Finiteset of nodes Transitions A C(PxT) and T. (TxP): subset of cartesianproduct between nodes P

W = a {1,2,3,..}: weigh of arcs M0 = Pi {1,2,3,..} node Pi: number ok tokens initials in each place

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

DEVS Formalism

M=<X, S, Y, δint, δext, λ, ta> where: X: set of input values. S: set of states values. Y: set of output values. δint: internal transition function.

δint :S

S

δext: external transition function. δext Q x X S , on

Q={(s,e)|sS, 0≤e≤ta(s)} set of states. e elapsed time from the last transition.

λ : output function: λ : S Y ta : time advance function.

ta: S

R+0∞

DEVS Processor example

M=<X, S, Y, δint, δext, λ, ta> on: X={job1, job2, .., jobn} S={job1, job2,.., jobn}U[Ø]xR+0∞ Y={y(job1), y(job2),.. Y(jobn)} δint(job,σ)=(Ø, ∞) δext(job,σ,e,x)= if job= Ø (x,tp,(x)) else (job, σ-e) λ(job, σ)=y(job) Ta(job, σ)= σ

SDL formalism

Object-oriented, formal language defined by The International Telecommunications Union as recommendation Z.100. Intended for the specification of complex, event-driven, real-time, and interactive applications involving many concurrent activities that communicate using discrete signals.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

SDL Formalism

The semantics of SDL was defined formally in 1988.

Standard Formal Graphical

and symbol-based Object-oriented (OO)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

GIS Data

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Celular automaton

Each cell xi(t) represent a state of the cell i in the instant t. k = {Empty, Static and Dynamic}

xi

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Game of life

The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Method

§ Cellular Automaton Structure

§ Evolution Rules

§ Precipitation phase § Settlement phase

• Data layers

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Soil (Ground)

•Return Flow •Kind of soil •Saturate •Kind of soil use •Non-Saturate •Percolation

•Runoff •Humidity •Evapo-trans piration •Temperature •Rainfall •Precipitation

Hydrologic resource

Meteorology

Factors/variables which affects hydrologic resource

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Where:

SWt is the total quantity of water in the soil (basin) (mm), SWi is the initial quantity of water in the soil (mm) Ri (rainfall) is the quantity of rain in the period of analysis(mm) Qi is the quantity of runoff (mm) ETi is the evapotranspiration (mm) Pi is the percolation (mm) QRi return flow(mm) t is the time expressed in days i is the index of cell

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

This expression gives us the water volume existing in each cell of our cellular automaton in a specific time t. In the expression, one position of the cellular automaton matrix [xi,xj] is represented by the cell xi.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

System diagram

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

States diagram

Update Receive

Send

Dynamic

Receiv e

Static

d en S te/ ei ec R ve

Update Receive

Receive

Send

Up

da

Send Update

Empty

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Static state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Empty state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Dynamic state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Discussion and future work

This slides depicts the first and more important step to construct a discrete hydrologic simulator based in a cellular automaton. It shows the specification of hydrologic model with the aim of simulate the behaviour of water in a river basin. The use of the cellular simplifies the mathematical expressions and helps us to maintain a structured view of the geographical data needed to represent the model. The use of SDL formalism simplifies the understanding of the behaviour of model thanks its graphical structure. The model is based in the hydrologic balance equation which permits calculate the quantity exist in each cell of the cellular automaton

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

References

[1] L. Ma, L.R. Ahuja, and R.W. Malone. Systems Modeling for Soil and Water Research and Management: Current Status and Needs for the 21st Century. ASABE, 1907-2007. [2] IPCC Intergovernamental Panel on Climate Change. Report IPCC. 2007. [3] J. Samper, M.A. Garcia, B. Pisani, D. Alvares, A. Varela, and J.A. Losada. Modelos Hidrologicos y Sistemas de Informacion Geografica para la Estimacion de Recursos Hidricos:. Confederacin Hidrologica del Ebro, VII, 2005. [4] E. Guzman, J. Bonini, and D. Matamoros. Modelo Hidrologico SWAT (Soil Water Assessment Tool) Prediccin de Caudales y Sedimentos en una Cuenca. Revista Tecnologica, 17:152–161, 2004. [5] Erik D. Zimmermann. Modelo Hidrologico Superficial y Subterraneo. Centro Universitario rosario de Investigaciones Hidroambientales. [6] A. López, D. Sauri, and Jose M. Galán. Urban Water

References

[8] P. Pea and J. Alirio. Sobre las redes de petri r-difusas. Divulgaciones Matemticas, 7:87–99, 1999. [9] Gabriel Wainer. Applying cell-devs methodology for modeling the environment. SIMULATION, 82:635, 2006. [10] Liu Qi and Gabriel Wainer. Parallel environment for devs and cell-devs models. SIMULATION, 83:449, 2007. [11] Fonseca P. and Casanovas J. 2005. ESS205, Simplifying GIS data use inside discrete event simulation model through m_n-AC cellular automaton; Proceedings ESS 2005. [12] ITU-T Telecommunication Standardization Sector of ITU. Languages and general software aspects for telecommunication systems. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU, 2007. [13] Zeigler, B. P.; H. Praehofer; D. Kim. 2000. Theory of Modeling and Simulation. Academic Press. [14] Petri, Carl A. 1962. "Kommunikation mit Automaten". Ph. D. Thesis. University of Bonn. [15] Recalde, L.; Teruel, E.; Silva, M. 1999. Autonomous

Thanks!

Pau Fonseca i Casas pau@fib.upc.edu

Technical University of Catalonia Barcelona School of Informatics Computing laboratory Barcelona Jordi Girona 1-3 (+34)93401773

We present an experiment that aims to simulate the figure of the broker, from the point of view of human behavior adding hunches and expectations too its decisions. As a starting point we use the paper of Veiga and Vorsatz, "Price manipulation in an experimental asset market”, a study that analyzes the influence on the behavior the other brokers due to an agent handle and a trading robot that performs with the intention of distort the market and thus, try to profit from their operations. In this research conducted by Veiga and Vorsatz, it is required the participation of real people whit the aim of analyze the behavior of the process of buying and selling a value in two separate rooms, one with the influence of manipulative agent and another without it. This article reproduces this experiment but using virtual brokers that belongs to a multi-agent simulation model. The main objective is to reproduce the experiment represented in the Veiga and Vorsatz paper using intelligent agents detailing the description of the psychological behavior of the brokers.

To execute the models you must download the zip files, extract it on a folder and execute the SDLPS engine. The file that rules the behavior of the model is SimBroker.sdlps. This file contains all the details of both models.

Remember that SDLPS can only be used to execute the models or for academic purposes. (POSAR LA LLICÈNCIA FORMAT UPC).

Attachment | Size |
---|---|

Low hunches model | 26.62 MB |

SDLPS simulator | 31.04 MB |