This document discusses using geo-historical modeling to simulate past disasters and catastrophes in order to better understand and manage risks. It notes that while digital information on recent events is abundant, information on past events is limited due to lack of digitization. Geo-historical modeling aims to address this by extrapolating available information to simulate digital models of past events and explore "what if" scenarios. This allows learning lessons from history to improve modern disaster risk management.

1. Archives
Geo-
historical
modeling of
1
Alexis
Drogoul
UMI
209
UMMISCO,
IRD/UPMC
alexis.drogoul@ird.fr
Simulating
the
past
to
better
manage
the
present:
geo-­‐historical
modeling
of
past
catastrophes
ISCRAM
med
2014
invited
talk

2. Vietnam
is
a
country
structured
by
water:
the
Red
River
delta
in
the
North
and
the
Mekong
River
delta
in
the
South
2
!"#$%& !'()*'+(,-'+.%")'(*)%/-*%#'0,*-'1)'(+.%$+'+2)1)'(
345,'6,'27%-/%(8)%*+9,6%+77)771)'(
!"#$%&$'()*$+",$- ./0/1"*(2/$32/)4$25$2/)$,)*6)$4)"$7)8)7$6(4)

3. Hanoi
is
a
city
literally
built
on
water
3

4. Flooding
in
Hanoi
is
menPoned
since
1000
years
(in
the
imperial
chronicles),
then
in
the
French
colonial
archives
from
1890
to
1954,
and
since
then
in
official
reports.
On
average,
1
major
flood
every
3
years.
4
2008 2014
2013
Recent
ones
are
mainly
caused
by
heavy
rain
episodes.

5. The
policy
against
flooding
has
been
constant
over
Pme:
building
dykes
systems
(~4000
km)
5
1927
2013
1905

6. «
Hanoi
ciPzen
and
city
planners
regularly
forget
they
live
near
a
river…
»
6
Flood
zone RiverDyke
West
Hanoi

7. The
analysis
and
transmission
of
past
disasters
is
an
integral
part
of
disaster
management
7
Prevention!
•Land use planning !
•Learning from events!
•Technical measures
The
experience
of
past
disasters
allows
local
knowledge
to
be
used
to
develop
community
responses
that
both
help
to
raise
awareness
of
risks
and
also
help
prepare
for
improved
future
disaster
response
and
reconstruc<on
Inspired by Integral Risk Management Cycle, FOCP 2012

8. Issue
1:
The
availability
and
accessibility
of
the
data
concerning
this
event
Issue
2:
The
construc<on
of
relevant
informa<on
from
these
data
Issue
3:
The
reconstruc<on
of
a
coherent
«story»
from
these
informa<on
!
This
is
what
historians
do,
but
it
would
be
helpful
to
be
able
to
do
it
in
a
more
systema8c
way
as
this
concerns
hundreds
of
thousands
of
events.
8
However,
being
able
to
learn
from
a
past
event
requires
addressing
some
issues

9. In
the
last
10
years,
informa8on
technology
has
become
ubiquitous
in
disaster
risk
management
and
there
are
hundreds
of
solu8ons
developed
!
!
!
!
!
!
!
!
!
!
!
!
But
they
require
the
availability
of
large
datasets
of
digital
informa8on
about
each
event
9
For
predic<ng
risks
For
assessing
risks
For
mi<ga<ng
risks
For
launching
alerts
For
educa<ng
people
For
organizing
rescue
....
!

10. Digital
informa8on
is
the
data
stored
in
computers,
which
can
be
automa8cally
harvested
and
analyzed
to
produce
useful
knowledge
about
a
disaster
10
From
real-­‐<me
monitoring... ...
to
post-­‐assessment

11. In
the
last
10
years,
as
soon
as
a
disaster
occurs,
rich
digital
informa8on
is
produced,
disseminated,
and
immediately
analyzed
11
Immediately
aGer
Fukushima,
572.000
new
TwiHer
accounts
have
been
created
in
Japan

12. today1900 20001800170016001000500 1500
However,
the
quan8ty
of
digital
informa8on
about
past
risk
events
is
strongly
dependent
on
when
in
history
they
have
happened
12
Past Future
Digi8za8on
of
physical
documents
Produc8on
of
digital
documents
cf.
F.
Kaplan,
2013,
hIp://Laplan.wordpress.com/2013/03/14/lancement-­‐de-­‐la-­‐venice-­‐8me-­‐machine/

13. today1900 20001800170016001000500 1500
A
first
step
can
be
to
make
more
informa8on
available
through
the
exploita8on
and
automated
analysis
of
available
digi8zed
contents
13
Past Future
Ins8tu8onal
analysis
(Web)mapping
Social
network
analysis
Social
network
analysis
Digi8za8on
of
physical
documents
Produc8on
of
digital
documents
Analysis
of
digital
informa8on

14. But
how
to
benefit,
for
past
events,
from
the
abundance
of
the
informa8on
on
contemporary
catastrophic
events
?
How
can
we
reproduce
the
dynamics
of
the
event
itself
so
as
to
beHer
understand
its
impacts
?
!
How
can
we
have
a
closer
look
at
the
social
dynamics
of
the
management
of
the
event
?
!
How
can
we
follow
the
behaviors
of
the
mul<ple
actors
of
an
event
in
order
to
understand
their
rela<onships
?
!
How
can
we
recreate
the
equivalent
of
Facebook,
Google
Maps,
YouTube,
TwiIer
for
past
events
?
(F.
Kaplan,
2013)
!
!
14

15. Geo-­‐historical
modeling
is
one
way
to
extrapolate
the
informa8on
available
in
order
to
«
tell
stories
»
and
produce
new
digital
informa8on
through
simula8ons
15
Past Future
Digi8za8on
of
physical
documents
Produc8on
of
digital
documents
Analysis
of
digital
informa8on
Simula8on
of
digital
models
3D reconstruction
Rialto neighborhood
in 1500 ab. based
on the documents
of Venetian archives
The diversity, amount and accuracy of the Venetian administrative documents are unique in Western history. By com-
bining this mass of information, it is possible to reconstruct large segments of the city’s past: complete biographies,
political dynamics, or even the appearance of buildings and entire neighborhoods. The documents are intricately
interweaved, telling a much richer story when they are cross-referenced.
Text recognition in
ncient hand-written
documents

16. Geo-­‐historical
models
are
not
supposed
to
be
faithful
reproduc8ons
(i.e.
«
movies
»).
16
Rather,
they
propose
to
reconstruct
fic8onal
reali8es,
suppor<ng
the
explora<on
of
what-­‐if
scenarios
(e.g.,
«
what
if
such
interven0on
op0on
had
been
chosen…
?
»,
«what
effect
this
decision
could
have
had
on
…
?»)
and
a
quasi-­‐experimental
approach
to
«
historical
truth
»

17. Research
works
on
geo-­‐historical
models
belong
to
rather
recent
trends
in
digital
humani8es
Geo-­‐historical
methodologies
Flooding
risks
in
Lyon
city,
C.
Combe,
J-­‐P.
Bravard
(Univ.
Lyon
2)
Simulation
of
Historical
Tsunamis
(Japan,
Taiwan,
US)
Virtual
archaeology
(Univ.
Of
Sussex),
«
Anasazi
Culture
»
(SFI),
etc.
!
Digital
History
«Venice
Time
Machine»
(EPFL)
!
!
Very
few
references,
however,
to
the
modeling
of
past
catastrophes
in
their
social/management
dimensions.
17
121
Fig. 27. L’inondation du Rhône en 1840.

18. The
ARCHIVES
project,
a
mul8disciplinary
approach
to
the
construc8on
of
geo-­‐historical
models
of
catastrophic
events
from
archived
data
International
Center
for
Advanced
Research
on
Global
Change,
VNU
(Geomorphology,
Hydrology)
IDEES,
Univ.
Rouen
(GIS,
hydrological
model,
Patrick
Taillandier)
Vietnam
National
Satellite
Center
(Red
River
basin,
Nguyen
Thi
Hoang
Anh)
!
National
Archives
Center
n°1
(Documents
and
data)
Ecole
Française
d’Extrême-­‐Orient
(History,
Olivier
Tessier)
IOIT,
VAST
(Digitizing,
Luong
Chi
Mai)
L3I,
Univ.
la
Rochelle
(Document
recognition,
Muriel
Visani)
!
IRIT,
Université
de
Toulouse
(Social
model,
Benoît
Gaudou)
IT
Dept,
University
of
Science
and
Technology
of
Hanoi
(GIS
building,
Nasser
Gasmi)
UMMISCO,
IRD
(Models
coupling,
Alexis
Drogoul)
18

19. ARCHIVES
is
organized
in
three
main
ac8vi8es,
with
two
outcomes
iden8fied
19
Chronology
and
scenarios
Stakeholders
GIS,
«
physical
»
models
Digitizing
&
analysis
of
documents
Reconstruction
of
geographical/
geophysical/hydrological
information
Geo-­‐referenced
index
Geo-­‐historical
simulations
Geo-­‐historical
model

20. The
first
proof
of
concept
focused
on
the
floods
of
July
1926
in
Hanoi
and
its
management
by
French
and
Vietnamese
authori8es
20

21. Delimita8on
of
the
case
study:
from
the
25th
to
the
31st
of
July,
1926,
in
Gia
Lâm
21
Breach at Gia Quất
28th, evening (old dyke)
29th, at 9 AM (new dyke)
Dykes
Breaches
Breach at Ái Mộ
29th, at 4 PM
Hà Nội -
downtown
Breach at Lâm Du
29th, between 4 PM and 5PM
Study area:
!
Gia Lâm (eastern
district of Hanoi).
!
Chronology:
!
- 25th to 30th of July:
increase of water height
(~12m) and main dyke
breaches
!
- 31st of July to
November: plugging of
dykes

22. First
task
was
to
gather,
digi8ze,
analyze
(and
some8mes
complement)
the
data
available
French
colonial
civil
archives
(NAC1
&
EFEO,
Hanoi)
French
military
archives
(Aix-­‐en-­‐
Provence)
Vietnamese
newspapers
(NAC1,
Hanoi)
Archives
of
technical
services
(water
management,
agriculture,
…)
(NAC1,
Hanoi)
City
Maps
(IGN,
France
&
NAC1,
Hanoi)
Vietnamese
imperial
archives
(NAC1
&
EFEO,
Hanoi)
Morphology
of
the
Red
River
bed
(VNSC,
Hanoi)
!
!
!
22

23. 6"
Contour lines
(brown)!
Buildings(red)! Red River (blue)!
Lakes (blue)!
The
second
task
consisted
in
linking
these
heterogenous
data
pieces
in
a
geo-­‐referenced,
8me-­‐indexed
database
23

24. This
allowed
to
produce
a
reasonably
realis8c
GIS
of
the
hydrographic/
urban/geomorphologic
condi8ons
in
which
the
flooding
event
took
place
24
6"
Contour lines
(brown)!
Buildings(red)! Red River (blue)!
Lakes (blue)!
The
addi<on
of
temporal
informa<on
allowed
to
query
and
navigate
the
database
and
get
an
idea,
locally,
about
the
«
<meline
»
of
the
event.

25. The
third
task
was
to
build
a
hydrological
model,
able
to
replicate
the
dynamics
of
the
Red
River
during
this
period
! GIS
Data
available
" Digital
Eleva<on
Model
(DEM)
" Shapefile
of
the
dykes
" Shapefile
of
the
buildings
" Shapefile
of
the
Red
river
" Shapefile
of
the
lakes
25

26. The
GAMA
plaiorm
was
used
to
implement
the
models
because
of
its
facili8es
for
handling
spa8al
data,
coupling
heterogeneous
models
and
ease
of
use
for
non-­‐computer
scien8sts
26
http://gama-platform.org
draw shape color: color depth:depth;
}
}
species red_river{
rgb color;
aspect geometry{
draw shape color:color;
}
}
species lakes {
rgb color;
int depth;
aspect geometry {
draw shape color: color;
}
}
species dyke parent: obstacle{
bool was_broken;
string break_date_str;
int month_break;
int day_break;
bool has_to_die;
bool is_flooded -> {cells_concerned first_with(each.water_height > 0) != nil};
bool is_about_to_be_flooded -> {water_pressure > threshold_to_be_flooded};
string commune_name;
float small_dyke_height <- 0.0;
int nb_step_flooded <- 0;
reflex breaking when: destruction_of_dykes and day = day_break and month = month_break {
do break;
}
action break{
ask cells_concerned {
do update_after_destruction(myself);
}
ask(commune where (each.name = commune_name)){
remove myself from: self.commune_dykes;
}
do die;
}
action compute_height
{
height <- dyke_height - min(cells_concerned collect (each.altitude));
}
user_command "Destroy dyke" action: break;
action split_dykes (float threshold) {
list<geometry> lines1 <- shape.geometries;
if (length(lines1) > 1) {
loop i from: 0 to: (length(lines1) - 2) {
geometry li <- lines1[i];
create dyke {
shape <- li ;
commune_name <- myself.commune_name;
do split_dykes(threshold);
}
}
shape <- last(lines1) ;
do split_dykes(threshold);
} else {
if (shape.perimeter < (threshold * 2) ) {
shape <- shape + 10.0;
do update_cells;
} else {
list<point> points <- list(shape points_on threshold);
list<geometry> lines <- [];
remove last(points) from: points;
geometry geom <- copy(shape);
loop pt over: points {
list<geometry> gs <- list(geom split_at pt);
add gs[0] to:lines;
geom <- gs[1];

27. The
model
designed
is
a
simple
diffusion
model
on
a
regular
grid,
which
could
be
easily
calibrated
using
historical
data,
and
could
easily
adapt
to
changes
in
its
«
environment
»
27
altitude
water height
height
height of the
highest dykes/
buildings located
on the cell

28. This
model
proved,
once
correctly
calibrated,
to
be
quite
accurate
(with
respect
to
the
occurrence
of
some
events,
like
the
breaking
of
dykes)
28

29. The
fourth
task
in
ARCHIVES
consisted
in
building
a
model
of
the
«
management
»
and
social
response
to
the
event
The
data
available
consisted
in:
!
-­‐
the
descrip<on
of
the
official
administra<ve
and
military
hierarchies
(Vietnamese
and
French
ones)
-­‐
the
iden<fica<on
of
the
key
actors
and
their
role
in
the
event
(through
reports
and
inves<ga<ons
led
aGer
the
event),
-­‐
the
flow
of
their
communica<ons
(leHers,
telegrams)
-­‐
and
various
other
pieces
of
informa<on
from
newspapers,
tes<monies
and
memories.
29

30. The
analysis
and
linking
of
the
documents
allowed
to
reconstruct
the
structure
of
the
command
chain
and
communica8on
flows
30

31. From
this
descrip8on,
a
«
social
model
»
of
the
actors
was
built,
focusing
on
understanding
how
the
flows
of
orders/informa8on
resulted
in
concrete
ac8ons
(building
of
small,
temporary
dykes)
31

32. A
number
of
simplifica8ons
were
necessary,
so
that
the
model
could
be
calibrated
and
easily
coupled
with
the
hydrological
model
(through
the
«
dyke
»
agents)
32
!
We
considered
for
instance
only
a
top-­‐down
order
and
a
bottom-­‐up
information/
request
chain,
using
FIPA-­‐ACL
to
manage
the
communication
protocols
between
agents

33. ARCHIVES
was
then
tested
during
a
7-­‐days
workshop
held
in
Da
Lat
(Vietnam)
in
July
2013
with
geographers
and
social
scien8sts
33
Par<cipants,
once
trained
on
the
basic
model,
were
encouraged
to
adopt
an
approach
based
on
hypothe<cal
reasonings,
which
resulted
in
a
number
of
addi<ons
to
the
basic
model
and
experiments.

34. A
number
of
«
historical
experiments
»
were
conducted
by
the
par8cipants,
among
them:
34
-­‐ understanding
and
modeling
the
dynamics
of
the
refugees
and
tes8ng
evacua<on
policies
-­‐ understanding
the
dynamics
of
the
resources
(material
ones,
like
bamboo
s<cks,
or
human
ones,
like
coolies)
-­‐ understanding
the
difference
between
the
official
descrip<on
of
the
command
chain
and
the
actual
communica<on
flows
observed
-­‐ …

35. ARCHIVES,
despite
it
being
quite
complete
now,
is
s8ll
a
preliminary
proof
of
concept.
• The
whole
project
has
proved
invaluable
in
• building
a
huge
dataset
(maps,
reports,
...
)
about
this
par<cular
event
in
a
comprehensive
and
focused
way
• providing
archivists
and
historians
with
new
ways
of
«
represen<ng
»
and
«
using
»
their
documents
and
knowledge
• providing
a
support
for
understanding
the
role
of
simula<ons
in
historical
research
(esp.
regarding
the
differences
between
theore<cal
and
actual
organiza<ons)
• However,
the
main
challenge
for
generalizing
this
approach
remains
the
transforma<on
of
raw
informa<on
into
digital
informa<on
• the
automatic
generation
of
actors
and
their
behavior
from
textual
documents
(e.g.
using
process-­‐mining
tools,
SNA…)
is
a
necessary
condition
to
address
different
events
35

36. The
general
perspec8ve
of
such
geo-­‐historical
models
is
to
provide
stakeholders
with
a
live
historical
fic8on,
which
can
be
used
as
an
experimental
framework
36
• For
tes8ng
prepara<on
or
management
op<ons
(including
«
modern
»
ones)
• For
comparing
these
op<ons
in
terms
of
consequences
on
society
• For
suppor8ng
the
work
of
historians
in
transmibng
the
memory
of
events
• For
building
interac<ve
and
easily
accessible
living
memories
of
these
events All
of
this
adding
to
the
«
digital
informa8on
»
available
with
the
goal
of
enhancing
the
awareness
and
prepara8on
of
contemporary
stakeholders
regarding
similar
risks