Download Evolution Knowledge Discovering and Information Modeling in Emergency Decision-making for Significant

Physical and Numerical Simulation of Geotechnical Engineering
3rd ISSUE, June 2011
Evolution Knowledge Discovering and Information
Modeling in Emergency Decision-making for Significant
Natural Disasters
QI Qingwen, ZHANG An, JIANG Lili
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources
Research, Chinese Academy of Sciences, Beijing, China, 100101
Laboratory of Agriculture GIS and Visualization, Informationization Center of China New Countryside, Chinese Academy of Sciences,
Beijing, 100101
[email protected]
ABSTRACT: With the features of unobvious auspice, outburst, potential derivative hazard,
significant natural disasters have great harm to the nature, society and ecological environment. To
construct a comprehensive management system for them, information processing and evolution
knowledge discovering is the basic and key issue, and emergency reaction is the core issue for the
natural disaster prevention. First of all, theoretic mechanism of significant natural disasters was
studied, which explained clearly the generating and evolution laws, the structuring basis and
boundary limit, and the spatial & temporal pattern of significant natural disasters in China.
Secondly, regional monitoring & warning system was designed and realized, including monitoring
sites for earthquake, forest fire, diseases, and ice-snow disasters, and the multi-source of data
collected from monitoring system was integrated and fussed into comprehensive data house.
Thirdly, discovering method for semi-structural or non-structural evolving knowledge of
significant natural disasters was set up and practiced, and variety of mathematic models were built
and then be used in situation simulation of the instances, taking earthquake disaster as an example.
Fourthly, multi-aim and group decision making modeling in emergency reaction was researched
and the balancing strategy was defined, taking ice-snow disaster as an example.
KEYWORDS: Significant natural disaster, Evolving knowledge discover, Information modeling,
Situation simulation, Emergency decision making
1
INTRODUCTION
Tens of significant natural disasters such as earthquake,
volcanic eruption, tsunami, landslide, forest fire, etc.
occurred in recent years, which caused tremendous loss of
human lives and social & economic property. With the
features of unobvious auspice, outburst, potential derivative
hazard, they have great harm to the nature, society and
ecological environment. So what we need is to construct a
comprehensive management system for significant natural
disasters, including diversified emergency agencies as the
operators, all functional phases as prediction & warning,
emergency reaction & direction, and reconstruction after
the instance, with all members participating the whole
procedure and dealing with all kinds of risks.
Among the above objectives and tasks, information
processing and evolution knowledge discovering is the
basic and key issue, i.e., to excavate potential knowledge
and laws from large quantity of monitoring and recording
data of disasters happened in the past thousands of years,
and reveal the inducing index of the instances, then to find
the precursor and abnormal phenomena of natural disasters
based on the inevitability factors beneath the occasional
factors. We can also uncover the coupling relationship
among natural, technological and artificial factors, which
© ST. PLUM-BLOSSOM PRESS PTY LTD
show the nature of derivatives, diffusibility, cause-effect
relationship of the significant natural disasters, from the
above data and phenomena.
Furthermore, emergency reaction is the core issue for the
natural disaster prevention. That is to say, when outbursting,
complicated and large scale hazards come, with inadequate
emergency preplan, we should build up emergency reacting
mathematic models, continuous or discrete, statistical or
dynamics, structural or semi-structural, combined with
non-structural knowledge induction, to set up multiple
solutions and plans for the governor to select and
compromise. So the information modeling in emergency
decision-making, based on the evolution knowledge
discovering, for the significant natural disasters, is the
major direction of natural disaster management in
Geo-spatial science & technology application.
2
THEORETIC MECHANISM
It’s important to research on the theoretic mechanism of
significant natural disasters because, firstly, they bear
complicated features to which human beings haven’t
thoroughly know their evolution laws; Secondly, the
Physical and Numerical Simulation of Geotechnical Engineering
3rd ISSUE, June 2011
regions where natural disasters occurred show unique
physical geographic features and different social-economic
characteristics; and thirdly, the overlay of their inherent
complexity with national particularity makes them more
difficult to be learned and dealt with.
The achievements of the theoretic mechanism are as
follows:
Generating and evolution laws of significant natural
disasters which obviously show regional, temporary,
natural, social and economic background, are studied and
concluded from abundant historical materials and data
about them in China. The results give us rational support to
expound clearly the spatial-tempo pattern of predictable
natural disasters in China.
The structuring basis and boundary limit of significant
natural disasters in China are analyzed according to four
types of objects as regional physical resources and
environment, society & economy, administration, and
emergency, and then the natural condition, bearing basis
and restriction boundary of the disasters are described. For
instance, ice & snow in Northern China is normal
phenomenon, while in Southern China where there is
seldom snow, it may result in heavy disaster, like the case
in January of 2008, which was the compound outcome of
coupling among natural, technological and human factors,
and was also the mixed result of regional geographic entity,
land-use status, population and transportation, etc.
The spatial & temporal pattern of significant natural
disasters in China are studied and summarized based on
horizontal & vertical discrepancy law of China, as well as
the specialty, locality and relativity of each region. We
could find which disaster occurred locally, which one
occurred in large scale; which one spread locally, which
one spread regionally, and which one spread in large scale.
As for the temporal pattern, we could find which disaster is
fast-fast mode, which one is the fast-slow mode, which one
the slow-fast mode, and which one the slow-slow mode.
3
MONITORING-WARNING
FUSION
AND
DATA
Significant natural disasters are generally incomplete in
information, and hard to monitor with lots of restriction
conditions in data collection. So how to successively obtain
new data from the objects, through remotely sensed image,
historical records and statistical data from various level of
government agencies, is the first and startup point for us to
launch an emergency procedure since the new coming
information shows the new symptom of the accident.
Our research activities as follow:
Set up the standard of data collection and data model for
significant natural disasters, including the acquisition
methods for the static data such as basic geographic map,
population map and economy map, etc., and also for the
dynamic data such as multiple-resolution remote-sensing
images, meteorological & ecological monitoring data, etc.
Fig.1 Regional Monitoring Network for Significant Disasters
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Evolution Knowledge Discovering and Information Modeling in Emergency Decision-making for Significant Natural
Disasters
DOI: 10. 5503/J. PNSGE. 2011. 03.005
(1) Modeling and fusion of the incomplete information
and isomeric data for the significant natural disasters in
order to build comprehensive data house including
databases of topography, resource & environment, history,
society & economy, remote-sensing image, and thematic
datasets of natural disaster records and emergency reaction
records. The isomeric data refers to earth observation data,
thematic observation data, optics and microwave data in
multiple resolution.
(2) Build the principle and model of automatic judgment
and correlation for the natural disaster information source
so as to evaluate the effectiveness and practicability of
those data, and then set up a coupling relationship between
dynamic data source and evolution model of the incidents.
(3) Design and develop a monitoring and warning system
consisting of four components, i.e., comprehensive
database,
monitoring-warning
software,
regional
monitoring platform, and users (see fig.1). The
monitoring-warning software component includes five
modules as dynamic monitoring, analysis-evaluation,
routine management, emergency reacting decision-making,
and maintenance. The monitoring platform integrates three
levels of Remote Sensing technologies on satellite, airplane,
ground tower. Take ice-snow disaster monitoring as an
example, we could extract snow cover information through
optics Remote Sensing data in non-cloud area, but use
microwave data in cloud area; For large scale area, we
could use lower resolution data such as TM/ETM, etc.,
while for concentrated area, use higher resolution data from
airplane platform. As for the temporal resolution, the lower
resolution such as one day could be used for large scale ice
& snow disaster monitoring, but the higher resolution such
as an hour for emphasis area should be booked with
program. According to the geographical difference within
China, we select four types of monitoring regions, i.e.,
earthquake regions including Northern China Plain,
Yunan-Guizhou Plateau, Sichuan Basin and Northwestern
Mountainous Area; forest fire region including Xin-Anling
forest area, Southwestern China forest area, Northern
Xinjiang forest area, and Eastern China tourism area;
ice-snow disaster region, mainly in Changjiang drainage
area and Northern Zhujiang drainage area; landslide region,
mainly in the mountainous area in Southwestern and
Northwestern China. The system is designed for five types
of users, i.e., government agencies, disaster preventing
organization, emergency management center, disaster
preventing research unit, and the masses. The whole system
is connected and run by two network as the professional
disaster preventing network and universal internet.
4
KNOWLEDGE
DISCOVERY
SITUATION SIMULATION
knowledge of them, semi-structural or non-structural, from
the above comprehensive database, combined with newly
collected dynamic data, based on theoretic mechanism of
them, and then simulate their situation in extreme
environment using virtual reality and computational
technology, so as to know vividly their generating,
developing and evolving process, as well as to acquire the
index of the whole, which is helpful to the prediction of
them.
Research items are as follow:
(1) To extract and discover the semi-structural or
non-structural evolving knowledge of the significant
natural disasters, such as spatial correlating rules, spatial
classification rules, spatial clustering rules, etc., by using
algorithms of correlating rules, data field & cloud
clustering, fuzzy clustering, neural network, genetic
algorithm, etc. in order to find major and key factors of the
incidents.
(2) To build mathematic models of tendency analysis and
prediction for significant natural disasters, and to construct
generating factors and models of the incidents.
(3) Situation simulation for significant natural disasters
in extreme conditions, i.e., those area with extreme weather,
and where human beings can’t reach to, such as earthquake
center area, forest fire spot, etc., based on the information
of range, intensity and classes of the incidents, so as to
know thoroughly their process, damage and trend, etc.
Take earthquake knowledge discovery and situation
simulation as an example:
The first part is to extract earthquake evolving
knowledge using correlation algorithm. At first, Apriori
five-step algorithm is operated to get candidate item set
which meets the need of least supporting frequency, so as
to form frequent item set Lk+1 from which to extract
correlating rules. Secondly, to make earthquake correlating
analysis to discover valuable information from plentiful
historical earthquake data, i.e., to transform earthquake data
into earthquake incident series set in specified
spatial-tempo range and limited earthquake magnitude, and
then to construct earthquake temporal similarity measure
model from three dimension of space, time and intensity.
Thirdly, to uncover process features and coupling
relationship of various factors of earthquake through token
characteristic analysis and data mining, and then to set up
monitoring & warning models in multi-approach integrated
mode for the coming use.
The second part is to realize situation simulation method
for earthquake, i.e. to build the structural model, define the
parameters and equation of the earth crust system so as to
make simulation analysis using quantified models for
virtual reality, and at last to compare and evaluate the
simulated result for the coming decision support system
development.
The system dynamics based earthquake imitation is
described in fig.2. Furthermore, a feedback relationship of
earthquake is presented by fig.3 which shows that an
earthquake is a complicated process driven by several
AND
Significant natural disasters often occur in extremely bad
conditions and in sudden mode, hard for human beings to
response, so what we could do is to extract evolving
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Physical and Numerical Simulation of Geotechnical Engineering
3rd ISSUE, June 2011
factors of geology, geomorphology, astronomy, crust
moving and human activities, and the disaster resulted from
earthquake is the combined product of earthquake
magnitude, focal depth, field condition, population density,
economic development, building quality, occurring time
and defending status, etc. On the basis of the above
systematic structure and feedback relationship, an
earthquake process is imitated according to conditions of
geography, geology, astronomy and auspice, therefore, to
quantify the relationship between damage losses with
earthquake magnitude and earthquake conditions.
Fig.2 Simulative Analysis Workflow for Earthquake Disasters Based on Dynamics Method
Fig.3 System structure and feedback relationship of earthquake disaster simulation
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allocation models, including addressing models with single,
multiple or specified restricted conditions, combined
models for definite multi-rescue points and continuous
multi-rescue points, models of least risk routing within
regional data network, models of most satisfied routing in
fuzzy network, and multi-rescue point models in indefinite
condition.
Multi-aim and group decision modeling for significant
natural disaster emergency response with regard to Chinese
domestic background including different geographic
regions and diversified regional social-economic conditions.
The method of multi-aim and group decision modeling is to
consider multiple factors such as physical, social-economic,
administration, technology and artificial factors in the
INFORMATION
MODELING
AND
BALANCING IN MULTI-AIM DECISION
Emergency reaction for significant natural disaster is a
kind of multi-aim and group decision making system, and
needs to integrate property losses, resources scheduling and
population relocation in decision process, because there are
to many impacting factors, and there’s often conflict of
interest among different departments and agencies. So it’s
essential to set up emergency decision making information
model and build a balancing mechanism for the reaction
scheme. Research work includes:
To build information models of emergency decision
making for natural disasters, mainly the addressing &
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Evolution Knowledge Discovering and Information Modeling in Emergency Decision-making for Significant Natural
Disasters
DOI: 10. 5503/J. PNSGE. 2011. 03.005
specified region, using fuzzy multi-aim Chebyshev model
and neural network & group-agent technology based
multi-agent decision model. The main idea is to invite a
group of decision makers, i.e. public or interest group, to
evaluate one or several candidate solutions or schemes
according to their preferences, and then balance or
compromise their choices basing on common rules, thus
result in group satisfactory solution or scheme.
Take ice-snow disaster as an example. It’s damage
mainly relates to society and economy, such as agriculture
and industry, transportation, electric power, etc. The
multi-aim decision making modeling should include such
factors as resources management and allocation, population
relocation, transportation line scheduling, electric power
dispatching. Then we can make buffer analysis on the
disaster area to get resource managing and scheduling
scheme and population relocation solution, and then make
combined buffer analysis on the basis of regional
transportation network and electric power data, so as to get
reasonable distribution for transportation flow and electric
power resource, and ensure the security of the people and
their property (see fig.4).
Fig. 4 Workflow of Emergency Decision Making System for Ice-Snow Disaster
Comprehensively, the workflow of evolving knowledge
extraction and emergency decision making modeling for
ice-snow disaster is described in fig.5. On the basis of
historical data and meteorological data, as well as the
national conditions such as regional differences and
temporal features, we understand the spatial-tempo
characteristics of ice-snow disasters. Then the ice-snow
disaster intensity prediction and spreading trend, including
the area and thickness of the ice & snow, are made with the
support of model base. Furthermore, with data collecting
system and the help of multi-data fusion model, we make
balanced and compromised multi-aim and group decisions
on ice-snow resource allocation scheme relating to
transportation, electric power and telecommunication, as
well as disaster rescue resources. Finally, the evaluation of
losses during and after the disaster is made.
6
decision making for significant natural disasters was
summarized and laid solid foundation for the latter
research.
(2) Monitoring and warning system of the significant
disasters was researched and realized, which includes
regional monitoring in four types areas as earthquake,
forest fire, diseases and ice-snow disasters respectively.
(3) Knowledge discovering and situation simulation
technology was developed and an example of earthquake
disaster simulative analysis based on dynamics was
described.
(4) Information modeling and balancing in multi-aim and
group decision method was researched, and an example of
ice-snow disaster knowledge extraction and emergency
decision making modeling was expounded.
(5) The method and technology introduced in this paper
have their advantages in the problem solving in the disaster
defense field, and show powerful tools of Geo-spatial
science & technology, although some faultiness exists and
needs further research.
CONCLUSION
(1) Theoretic mechanism of evolution knowledge
discovering and information modeling in emergency
26
Physical and Numerical Simulation of Geotechnical Engineering
3rd ISSUE, June 2011
Fig. 5 Comprehensive workflow of the method & technology for Ice-Snow Disaster
on the multi-distribution and the major influencing factors
on SARS in Beijing 2003, China Journal of Epidemiol, 2005,
26(3):164-168 (in Chinese).
[8]. Lili Jiang, Qingwen Qi, An Zhang, 2007, Color on
emergency mapping, Proceedings of SPIE--Vol.6751,
Geo-informatics 2007: Cartographic Theory and Models,
Manchun Li, Jiechen Wang, Editors, 675104.
[9]. An Zhang, Qingwen Qi, Lili Jiang, 2007, Study on
Emergency Mapping Technology in Rapid Response,
Proceedings of First International Conference on Risk
Analysis and Crisis Response, SPIE, IE pending.
[10]. An Zhang, Qingwen Qi, Lili Jiang, 2007, Research about the
Location Technologies of Forest Fire Detection Based on
GIS. Proceedings of SPIE--Vol.6754 Geo-informatics 2007:
Geospatial Information Technology and Applications, Peng
Gong, YongXue Liu, Editors, Aug. 2007 (EI Pending).
[11]. An Zhang, Qingwen Qi, Lili Jiang, 2007, GeoRSS Based
Emergency Response Information Sharing and Visualization,
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