Download Evolution Knowledge Discovering and Information Modeling in

Evolution Knowledge Discovering and Information Modeling in
Emergency Decision-making for Significant Natural Disasters
QI Qingwen1,2, ZHANG An1, 2, JIANG Lili1, 2
1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical
Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China, 100101
2. 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 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
227
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 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:
(1) 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.
(2) 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.
(3) 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 and Data Fusion
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:
(1) 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.
228
Fig.1 Regional Monitoring Network for Significant Disasters
(2) 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.
(3) 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
229
coupling relationship between dynamic data source and evolution model of the incidents.
(4) 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 and Situation Simulation
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 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
230
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 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
231
5
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:
(1) To build information models of emergency decision making for natural disasters, mainly the
addressing & 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.
(2) 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 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).
232
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
Conclusion
(1) Theoretic mechanism of evolution knowledge discovering and information modeling in emergency
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.
233
Fig. 5 Comprehensive workflow of the method & technology for Ice-Snow Disaster
References
[1]. Paul Stolorz and Christopher Dean. (1996) Quakefinder: A Scalable Data Mining System for
Detecting Earthquakes from Space. KDD-96 Proceedings.
[2]. ZHU Bingbing, WU Shaochun, WANG Wei, Application of dominance rough set in earthquake
data extraction, Computer Applications, 2006, 26(12):3023-3026. (in Chinese)
[3]. WU Shaochun, HU Huashan, A data extraction network for earthquake and its scheduling, Journal
of Shanghai University (Natural Science Edition), 2006, 12 (5 ) : 529-534 (in Chinese).
[4]. WANG Wei, LING Mingzhao, MA Qingzhong, The data mining and its prospect of application in
earthquake forecast, Recent Developments in World Seismology, 2005, 23 (12):1-13 (in Chinese).
[5]. LIU Dongyun, GUO Qing, ZHANG Chunxi, Report management system of relief disaster and
prevents disease in China, 2003, 19(6):496-498 (in Chinese).
234
[6]. SHI Yaolin, Dynamics random model for SARS Spreading, Chinese Science Bulletin, 2003,
48(13):1373-1377 (in Chinese).
[7]. WANG Jinfeng, MENG Bing, ZHENG Xiaoying, Analysis 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, Proceedings of the 3rd International Conference on Semantics,
Knowledge and Grid, OCT. 2007 (EI Pending).
[12]. An Zhang, Qingwen Q. Symbology in the Forest Fire Emergency Map. The International Archives
of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8.
Beijing 2008. p. 457 ff.
235