=Paper=
{{Paper
|id=Vol-1535/paper-03
|storemode=property
|title=Multi-scale modelling for simulating marine activities under heterogeneous environmental constraints
|pdfUrl=https://ceur-ws.org/Vol-1535/paper-03.pdf
|volume=Vol-1535
|authors=Annalisa Minelli,Cyril Tissot,Mathias Rouan,Matthieu Le Tixerant
|dblpUrl=https://dblp.org/rec/conf/sageo/MinelliTRT15
}}
==Multi-scale modelling for simulating marine activities under heterogeneous environmental constraints==
https://ceur-ws.org/Vol-1535/paper-03.pdf
Multi-scale modelling for simulating marine
activities under heterogeneous
environmental constraints
Annalisa Minelli1, Cyril Tissot1, Mathias Rouan1, Matthieu Le
Tixerant2
1. UMR 6554 LETG-Brest Géomer, Institut Universitaire Européen de la Mer,
Place Nicolas Copernic, 29280 Plouzané, France
Annalisa.Minelli@univ-brest.fr
Cyril.Tissot@univ-brest.fr
Mathias.Rouan@univ-brest.fr
2. Terra Maris
Technopôle Brest-Iroise/Hameau d’entreprises, 29280 Plouzané, France
Matthieu.Letixerant@terramaris.fr
ABSTRACT.This paper describes the concepts behind the implementation of a multi-agents
model aimed to explore how marine activities respond to various environmental constraints.
The methodology takes advantage on a responsive agent-based structure, and treats the
environment as a set of forcing variables (biophysical, socio-economic and regulatory data).
A first experiment in the Iroise Sea area shows a great potential in assessing the intensity and
the variability of marine activities at different scales level. The whole methodology is
presented in this paper in order to completely analyze the contributions and limitations
concerning the SIMARIS prototype.
RESUME. Cette contribution décrit les concepts associés à la mise en œuvre d'un modèle multi-
agents simulant le déroulement d'activités marines sous contraintes d'environnement. La
méthodologie proposée se base sur un environnement de simulation permettant d’intégrer des
données spatio-temporelles multi-sources et multi-échelles au sein d’un modèle à base
d'agents contraints. Un premier prototype développé en mer d'Iroise montre les apports
possibles de cette démarche pour évaluer la variabilité des activités de pêche à différentes
niveaux scalaires.
KEYWORDS: multi-scale modelling, agent-based model, marine activities, spatio-temporal
constraints, human-environment.
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1. Introduction
A review of the various scientific contributions concerning the modelling of
human-environment interactions shows how difficult it is to integrate spatial and
temporal dimensions into a multi agent based approach (Gould, 1987; Stonebraker,
1990; Allen, 1991; Muxart, 1992; Snodgrass, 1992; Cheylan, 1993; Claramunt,
1999; Parent, 1999; Legay, 2000; Pelekis, 2005; Tang, 2008). The main limitation
results from the variability of these interactions. In fact, ecosystem dynamics are
not always synchronised with the evolution of human activities, which respond to
far more complex cycles that are particularly difficult to model. Although this
human-environment “desynchronization” may seem obvious, most multi agent
based models run in environments where all inputs and/or outputs are considered
known and all the events hold the same rhythm.
MAS-based approaches are, therefore, inapplicable when the system encounters
a situation unexpected by the model designer, i.e. when problem specification is
incomplete or the environment undergoes changes affecting human activities. This
limit is underlined by the complexity of multi-scale interaction between human
activities and environment. This article proposes a new methodological approache
that can simulate marine activities at different scales in order to take into account
these complex interactions.
2. Conceptual framework
Our methodology is based on a cross-cutting approach focused on combining
multi-agents model with multi-scale spatio-temporal databases with the aim to
model interactions between human activities and their environment. This approach
has a two-pronged goals: the simulation of the impact of human activity on a given
environment and the assessment of the capacity of activities to adapt to
environmental changes. Moreover in the paper we will point out how, using these
modeling techniques, the representation of the interactions between activities and
between activities and environment, is easily accomplished and the individuation of
potential pressure zones (in terms of resources exploitation) and conflict zones
(between activities) is possible.
2.1. Environmental Modelling
Environment modelling is a critical point in multi-agent models if it involves
natural phenomena. In computing terms, the environment is considered as the
support system component of simulations (Ferber and Müller, 1996). It thereby
manages access to resources. In ecological modelling terms, it refers to the
formalisation of how a natural environment operates, leading to the closest possible
abstraction of reality (an interesting example could be find in Matthews et al., 2007).
Computer modelling is restricted by natural environment features whereas
ecological modelling is restricted by programming language structures and methods.
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Multi-scale modelling for simulating marine activities 3
In agent-based systems, environmental modelling is an extremely important task
yet paradoxically often disregarded (Weyns et al., 2007). According to Weyns et al.
(2007), the environment represents a first-class abstraction.
MAS-related studies that give less importance to the environment focus mainly
on the role, capacities, interactions and modelling of agents. In contrast, ecological
modelling-related studies specifically involve the regulations and processes that
govern ecosystems in order to study their impacts on ecosystem balance (Pereira et
al., 2006; Pereira et al., 2009, Wang et al., 2008). Odell et al. (2002) define a MAS
environment as the constraints under which agents exist.
In our approach, the environment consists of a set of spatial data. In particular, it
includes constraints that influence the development of an activity, so it influences
directly or indirectly almost all the decisions and/or actions taken by agents.
2.2 Integration of spatio-temporal variables
Time constitutes a core element in any dynamic modelling approach. The
temporal dimension can be comprehended linearly (occurrences with chronological
development) or discontinuously (cyclical or random occurrences) (Moellering,
1976; Muxart. et al., 1992; Peuquet, 2002; Edsall et al., 2005). For what concerns
human activities, time is generally associated with a [given] state of the environment
(landuse, landcover, resources etc.). In addition to exploring the chronological
progression of environmental change, the analysis focuses on the space-to-time
ratio. Concerning spatial data integration, recent models often ignore the interactions
underlying geographical objects (Berjak et al., 2002, Jantz et al., 2005, Marceau et
al., 2008). Moreover, the great part of modelling approaches considering the
analysis of the dynamics of human-environment interactions, integrate space only as
a structural element. It is clear that this assumption, drastically limits the spatial
resolution and possible applications of the model itself because it requires that all
the rules, governing the evolution of the simulation domain, must be specified in an
explicit or probabilistic way. So, due to the intrinsic variability of both natural and
human systems, this type of approach cannot adequately take into account the
complexity of human-environment interactions. The impact analysis of human
activities on the environment requires a close connection between the notions of
time and space. This relationship determines the various states of the space
observed, by considering the physical or human factors guiding the changes of that
space. The space-time relationship, in this context, include the spatio-temporal entity
concept (Cheylan et al., 1993; Hornsby et al., 2004; Cheylan et al., 2007). In
conceptual terms, several authors have suggested that the orthogonality of the
spatio-temporal entities is a key criterion to the integration of hierarchies in a
modelling approach (Claramunt et al., 1999; Parent et al., 1999). These studies show
also that the evolution of the nature and structure of a spatial entity is associated
with a time scale that determines state changes. In a human-environment
relationship analysis, this time and space scale variability has a direct effect on the
intensity and nature of interactions. Orthogonality therefore allows the joint
consideration of two different scales:
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4 SAGEO’2015
• a temporal scale, where the various cycles can be decomposed into as many
time steps of variable duration as necessary;
• a spatial scale, where the evolution of geographic entities at a given
aggregation level is determined by the conjunction of human and natural
events (each of which is conversely constrained by specific temporalities).
In this context, the integration of geomatics tools (as digital geographical data
processing, spatio-temporal databases etc.) in model design can facilitate the
development of a modelling platform that can determine a complex simulation space
by combining a set of human (socioeconomic, regulatory forcings) and
environmental (physical and environmental forcings) factors.
3. The SIMARIS prototype
The SIMARIS (Simulation of marine activities under heterogeneous
environmental
constraints) prototype is designed to describe human activities and their spatio-
temporal distribution, modelled in the form of responsive agents constrained by
exogenous variables (biophysical, socioeconomic and regulatory constraints). It
considers the ecosystem studied as a resource potentially used by humans according
to the techniques available and to their social organisation at a given time. This use
is not always associated with natural resources exploitation; it can also refer to land
allocation for a specific use.
For example, manure-spreading practices are associated with a farming area for
which the potential use of the practice itself has a set of spatial and temporal
constraints. Each activity thus responds to a specific operating cycle, adjusted
accordingly to technical, economic, regulatory, environmental and social filters.
In parallel, the analysis of the variability of environmental components entails
time fragmentation into a series of intervals more or less long according to the
processes they are associated with.
3.1 General principle of SIMARIS
The SIMARIS model provides a simulation environment that can integrate
multi-source and multi-scale spatio-temporal constraints as forcing variables within
constrained agent models (Tissot et al., 2013, Le Tixerant et al., 2012). This
approach meets the requirement of explicitly formalize spatio-temporal relationships
between spatial entities, environmental processes and human activities. The aim is to
set up a methodological framework that can run without a complete system
specification, i.e. considering the modelling environment as the result of a set of
multi-scale constraints. SIMARIS has been implemented in GAMA simulation
platform.
The GAMA multi agent platform (Grignard et al., 2013) is a quite recent multi
agent protocol able to fully integrate geographical data in a multi agent system
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Multi-scale modelling for simulating marine activities 5
(Taillander et al., 2014). It is written in Java and can be parametrized in .gaml
language. This specific platform has been chosen for this work in reason of its large
compatibility with geographical data. In fact it allows the reading of georeferenced
files by their projected coordinate systems specification and their automatic
reprojection from different coordinate reference systems if necessary. This is quite
unusual for a MAS, especially considering that MAS execute operations in a
synthetic world, measured by the (X, Y) cartesian coordinates instead of (East,
West) or (Lat, Long) coordinates, as Geographical Informative Systems do.
Moreover it allows the multi-level modelling, which means that different analyses
can be executed contemporarily in the same environment but with a different space-
time resolution and this is different from a mere parallel computing, since these
“levels” of analyses can influence each other. In case of multi-level analysis the
agents can be organized in super-agents: a sort of macro-entities which generalize
the characteristics of a group of affine agents and, conversely, the single agent
inherits the characteristics of the macro-agent.
All these features makes GAMA suitable for the SIMARIS prototype
implementation.
3.2 Spatio-temporal granularity
Defining a spatial granularity, necessary to model human or environmental
events, is one of the key issues and an essential precondition to build a model with
spatio-temporal constraints (Pereira et al., 2004). A relatively intuitive, commonly
used practice is to consider the smallest common denominator of the objects
involved as the model’s spatial unit. A similar practice is used for the simulation
time step. While this practice provides good results in many applications, its
drawback is that it tends to involve objects with unknown dynamics or no tangible
reality. For instance, in the case of marine activities, the knowledge of a procedure
performed on a daily scale does not necessarily reveal its hourly course.
In this context, the SIMARIS model use spatial aggregation and disaggregation
procedures that access data to match the level scale of the various sub-models used
in the simulation. This issue raises important methodological questions like carrying
out scale transfers within models. Scientific contributions dealing with this type of
approach are rare and highlight the complexity of spatial aggregation and
disaggregation procedures (Gotway, 2002; Duboz, 2004). The main restrictions is
the loss of consistency among data (which can be homogeneous at a given scale, but
heterogeneous at different scale). It is therefore necessary to control small-scale
variability in the models used, either explicitly or in a parameterised form.
Given the diversity of the data representing constraints for the models developed
within the SIMARIS model (statistical data, digital geographic information, e.g.
raster and vector data, temporal data) and the heterogeneity of the spatial
organisation levels involved, the methodology developed is based on a bottom-up
approach. The aim is to hook several abstraction levels in the model based on the
higher resolution one.
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Regarding the multi-scale approach, in GAMA it is possible to define different
spatio-temporal resolution, both in the same level of analysis and through different
levels. In fact, different spatial resolutions can be specified for different grids and
the cells can have squared, rectangular and hexagonal shape. Regarding time,
different temporal granularities can also be specified for different agents in the way
that any possible desynchronization between agents’ activities can be fully
represented.
Once the first and higher resolution level analysis is simulated, GAMA can
aggregate agents in super-agents, as hinted before. This means that if, for example,
the SIMARIS model simulates different agents for different types of fishing boats in
the first level (for example “algae fishing boat”, “king scallop dredge fishing boat”,
etc.), at the second level (more aggregate) it could appear a super-agent “fishing
boat” grouping all types of fishing boats. And informations coming from the first
level simulation can be remounted to the second level of simulation performing an
aggregation operation too. Since GAMA is fully configurable, it is then possible to
choose for each level the informations to be remounted and the better aggregation
method to be chosen.
3.3 Application and results in the Iroise Sea
3.1.1. The Studied Zone
A first implementation of SIMARIS model in GAMA platform applies the
principles described above on the Iroise Sea. In fact, this zone is a Natural Marine
Parc and a Marine Protected Area (Figure 1) hosting concurrent (in space and time)
activities, potentially generating pressure on natural environment, exploiting natural
resources and interacting one with each other.
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Multi-scale
scale modelling for simulating marine activities 7
Figure 1.the Natural Marine Parc area in the Iroise Sea.
3.3.2 Input data
Following the bottom-up
bottom approach, we choose to start the simulation ion by defining
all the single activities in detail. At the first level of simulation (higher resolution),
two different types of fishing activities are taken into account: the algae and the king
scallop dredge fishing. A brief summary of input data and its its origin is reported in the
table below.
Table 1. Type and origin of input data
Geographical Data Not Geographical Data
Maximum capture rates,
Comingfromfishingcommittees Fishing Zones
fishing calendars
Bathymetry, Tide,
Comingfromother sources
ports location king scallop growing rules
3.3.3 Species implemented in GAMA
In SIMARIS model, a set of agents is implemented in order to simulate fishing
activities. Each element implemented as agent is supposed to be able to interact with
the environment and other agents following internal and external constraints and
factors (weather conditions, regulation, fish market price…).
Regarding the limits and the shape of the “world” (here intended as working
zone), they are defined by the bathymetry.
bathymetry. So, since bathymetry represents the
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environment, it means that automatically it cannot be influenced by the behavior of
all other agents or by external occurrences. This assumption fits quite well the real
behavior of the earth surface, since, unless of severe and rapid natural changes (as
big earthquakes, for example), it can be considered in “stable” condition. All the
elements acting on the environmental scene are defined as agents.
Since the simulation regards the algae and king scallop dredge fishing activities,
two different agents “boat” are defined: one for each type of activity. Both the
agents implement a set of rules in order to simulate moving and fishing acts:
• the first action (implemented in GAMA as “reflex”) is the movement from
a starting port to a random point of the fishing zone. This reflex is activated
by the conditions for the agent to be initialized, to be in a fishing period and
to have all its fishing potential still intact (the boat has enough space to
accommodate the catch). Once this first action is executed, the boat changes
state from 0 (inactive) to 1 (active).
• the second reflex simulates the fishing activity. This reflex is activated if
the boat is active (state = 1) and it has the possibility to accommodate fish.
Starting from the random point of the fishing zone, the boat tries to catch all
that it can; a calculation of the catch is made basing on the quantity of fish
present in the specific cell of the fishing zone so that the boat starts to load.
If the boat fulls after the fish, it modifies the state parameter to 3
(discharge) and activates the discharge reflex. If the boat is not full after
have visiting a specific cell of the fishing zone, it selects the neighbour cells
and it chooses one of them as fishing target. The state value is turned to 2
(fishing). The process continues since the boat is not full.
• the third reflex simulates the action of discharge for the boats and it is
activated when the boat is full and its state is set to “discharge”. In this
reflex the boat goes automatically to the nearest discharge point, it
discharges the catch and modifies its state to 1 (inactive) so that the
moving/fishing loop can continue.
Another particular characteristic of the agent “boat” is that the captain has a
memory. In fact, once he catched all the possible from a fishing zone cell, he stores
the visited cell in a list in the way that, during the fishing movement, he drops the
visited (and empty) cells from the list of neighbours, excluding them from the
possible target cells.
The agents “fishing zones” are differentiated in reason of the type of activity.
The resource is stored in a regular square cell grid, perfectly sized on the input
vector area of fishing zones. The resolution of this grid is automatically connected to
the extension of the studied area. Each cell carries information regarding the
quantity (nb. of individuals), size and weight of each individual. The exemplaries
are differentiated in reason of the stage of growth too. At the beginning of the
simulation a random quantity and distribution of individuals are simulated, size and
total weight are calculated consequently for each cell.
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Multi-scale modelling for simulating marine activities 9
For example, for the king scallop, there are three different stages of life: young,
growing and mature. The stages have a respective duration of 180, 365 and 365 days
(Buestel and Laurec, 1975; Buestel et al., 1986; Chavaud et al., 1998). The boat
catches as much as it can. and if the boat catches all the resource in the cell (all the
exemplaries) the cell becomes empty, if the boat catches less than the cell content,
the remaining part of resource is left in the cell. The regeneration of the resource is
periodically executed once or twice a year (seeding of juvenile in some fishing area).
The agent “tide” is a regular grid covering all the bathymetry extension with the
same spatial resolution. In reason of the zone examined, the grid is appropriately
resized and, accordingly to the nearest located port, the value of tide is read from the
database Postgres. Thanks to a specific set of functions, called “SQLSKILL”,
GAMA is able to connect with the database and read values from tables, executing
SQL queries. For each time step, a request is formulated to the database Postgres
and the corresponding value for tide is read from the table, the value is then stored in
each cell of the tide grid and it changes at each time step. If the zone studied is quite
large and more than one port is included into the zone itself, the values of tide for
different ports are read and the obtained values are interpolated in order to simulate,
by means of a continuous surface, the spatial variability of the tide.
Tide values infer fishing activities, in fact, depending on the fishing type, there is
a range for the height of water which maximize the catchment. This threshold value
is used if fishing zones are not provided or available in the working area, in joint
with the bathymetry grid and an eventual seafloor bed grid, in order to individuate
potential fishing zones.
Moreover the model contains an agent “traffic” which has the shape of a regular
grid. This grid too is resized once the working zone is individuated and its task is to
record the passage of a boat adding a “+1” in each single cell visited by a boat. This
agent is useful in order to quantify the effective pressure on the environment (due to
frequentation), once a specific scenario is simulated.
3.3.4 Procedure description
The SIMARIS model is dynamic and multilevel. This means that the first thing
asked to be specified is the spatial and temporal limits of simulation. A start and end
datetime must be inserted into the graphical interface, and the working area is
described by two couple of coordinates, belonging to two points, delimiting a
rectangular zone.
On the basis of spatial extension (area of the working zone) is assigned a level to
the analysis, considering that, for each different level, different type of informations
will be returned from the model. For example, if the spatial extent of the analysis is
the fishing zone (or a part of it) and the period is one week, probably the interest of
the simulation is to monitor the single catchment act, so the spatio-temporal
resolution of the analysis must be appropriate, and the information obtained will be
quite detailed (e. g. catchment rate for each single fishing session, view of the boat
movement during the catchment etc. etc.). As opposite, if the simulation covers one
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10 SAGEO’2015
year and it is performed over all the Iroise Sea, the interest could be to monitor the
maritime traffic and the global catchment rates each month, for example, and it
makes no sense to view the movement of boats during a single fishing session.
Another model peculiarity is the possibility to represent desynchronization
between agents. In fact it exists (and must be specified) a global “step” parameter,
which represents the temporal granularity and the time step corresponds to a cycle.
But it is also possible to define an optional “step” parameter for each single agent.
This means that if, for example, our aim is to simulate the growth of species (which
is regularly a quite long process) and the fishing activities at a different velocity, we
can do it. Although this instrument is potentially very powerful, it must be used
carefully since an unnecessary desynchronization could invalidate the results of an
entire simulation: for example it is fundamental to synchronize fishing operations
(which should be represented at hourly or half-an-hour rate) with fishing calendar
(which can be represented at hourly but also at daily or monthly rate).
So, different values for time and space granularity are automatically defined.
The models’ input layers (spatial data) are: a general vector map of the studied
zone; the raster file of bathymetry; the vector point map of ports; the vector maps of
fishing zones (optional); the vector map of seafloor bed (optional).
Other models’ input data are: the database of the tide table; the name of the
port(s) to be used to calculate tide level(s); a starting and ending datetime of
simulation; the coordinates of two points defining the working zone.
Once all the inputs are specified, the model guides the user, by using messages
on the console, through a step by step procedure. For atypicalrun:
1. the model initializes both the environment and the agents; a general
skeleton map of the zone reporting starting and discharge points of ports
appear. The user is invited to specify the zoom level by reading two couple
of coordinates on the map (Figure 2). In this case the presence of two types
of agents «boats» are examined: three algae and two king scallop fishing
boats. The time period examied is one week and the extension of the
selected zone allows the most detailed level of analysis, so, the movement
of the single boat and resource consumption will be evidenced during the
routing;
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Multi-scale
scale modelling for simulating marine activities 11
Figure 2.from the top: the “world” map with zoom on the working zone, the GAMA
console and the “Parameters” section.
2. all the input layers and the entire world are resized on the specified zoom
and the agents boats, tide and traffic are created;
3. the species are randomized over the fishing zone grid and they start
growing. The boats start moving and fishing, if the calendar allows it
(Figure 3 a, b).
A typical situation after some steps is the one evidenced in Figure 3c. On the
higher part is reported
orted an extract from the king scallop fishing zones attribute table
and it is possible to see different parameters belonging to each single cell (the
number of individuals in each cell, their age, their stage of growth and the weight of
the single individual);
ual); on the bottom part is reported the fishing zone situation as it
appears after some cycles: in light orange the cells containing some resource, in blue
the empty cells. From this figure it is possible to evidence that there are some parts
of the fishing
ng zone most frequented than others (e. g. the ones circled in red).
These first results could be used, for example, in order to extract some important
information about frequentation and resource exploitation, or they can be crossed
with other data (map of more sensible zones - at naturalistic level) in order to
extrapolate potential pressures on natural environment. Moreover an unforeseen
event could occur (a modification on fishing calendar or the interdiction of a part of
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12 SAGEO’2015
the fishing zone) and a new scenario
scenario configuration could lead to sensibly different
results.
Figure 3.on the top: an extract from the fishing zone area attributes: the
idividuals present in each cell have all the same growt stage (lifedays and stage) but
they are numerically different (nbIndiv); on the bottom: (a) a boat fishing. In blue
are evidenced visited and empty cells; (b) a boat at discharge place; (c) the resized
fishing zones (light orange) accordingly to the specified zoom. Circled in red: an
example of most exploited
exploit zones.
It must be said that in this case, since the example is aimed to individuate the
more exploited zones (into a previously defined fishing zone) and to obtain a
resource balance, a small geographical zone is selected and the analysis level is the
most
ost refined. If a longer period and a wider zone is selected, the model gives
aggregate information as requested from specifications. For example, if the zone
contains more fishing zones we will not see any boat moving but we will obtain in
output a resource
ce exploitation graphic which will synthesize the fishing activity for
all the boats over the specified period for the specific zone. So, detailed data are
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Multi-scale modelling for simulating marine activities 13
«hidden» since a high level of refinement is not required, synthesis data is produced
instead.
4. Conclusions
The approach presented in this paper explore new methodological framework in
multi-scale modelling. Developed in GAMA simulation platform, the SIMARIS
prototype explicitly formalizespatio-temporal relationships between spatial entities,
environmental processes and human activities. The complexity of the prototype is
still limited, since the model takes into account for the moment only fishing activity
(super-agent) and details some of them (agents), so there are two different analysis
levels.
In thematic terms, the applications to coastal area situations, provide
encouraging results for assessing complex interactions between marine activities and
coastal ecosystems. Changes in activity intensity will be particularly interesting to
study because they illustrate the variability of these interactions.
Since it involves human activities under heterogeneous spatio-temporal
constraints, the approach described here require the development of models
integrating many stochastic processes. The validation of an approach combining
deterministic and stochastic modelling is therefore a key issue in the implementation
of the SIMARIS model.
Finally, the approach described here raises numerous questions as, for example,
how to structure a multi-scale model like SIMARIS. The applications mentioned
above indicate that there is a relationship between the granularity of the spatial and
temporal data and the abstraction level of the model. The formalisation of this
relationship remains complex. Extensive exploration of model structure may help to
describe this relationship.
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