WO2011069613A1 - Dispositif et procédé d'attribution de degrés d'avertissement sur la base de risques - Google Patents

Dispositif et procédé d'attribution de degrés d'avertissement sur la base de risques Download PDF

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Publication number
WO2011069613A1
WO2011069613A1 PCT/EP2010/007298 EP2010007298W WO2011069613A1 WO 2011069613 A1 WO2011069613 A1 WO 2011069613A1 EP 2010007298 W EP2010007298 W EP 2010007298W WO 2011069613 A1 WO2011069613 A1 WO 2011069613A1
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Prior art keywords
warning
data
hazard
levels
predefined
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PCT/EP2010/007298
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German (de)
English (en)
Inventor
Joachim Post
Kai Zosseder
Stefanie Wegscheider
Ulrich Raape
Matthias Mück
Sven Tessmann
Christian Strobl
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Deutsches Zentrum Für Luft- Und Raumfahrt E.V. (Dlr E.V.)
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Publication of WO2011069613A1 publication Critical patent/WO2011069613A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes

Definitions

  • the invention relates generally to an apparatus and method for computer-aided allocation of alert levels to individual regions of a target region, the alert levels indicating the threat to a region from a natural or technical hazard event.
  • the invention relates to an apparatus and a method for the computer-assisted, risk-based assignment of predefined warning levels to spatial warning units of a target region segmented into spatial warning units.
  • An early warning system is generally used in time to warn of dangerous events, especially natural disasters - but also of dangerous events of a technical nature, such as a meltdown - which require protection of the environment and people, facilities and goods, which can be achieved, for example, by timely evacuation .
  • Natural disasters include, for example, those of tectonic origin, such as earthquakes or tsunamis, as well as natural disasters of climatic origin, such as tornadoes, landslides or snow avalanches.
  • the present invention may find use in early warning systems for such natural disasters or for technical hazards events.
  • An early warning system after detecting a danger event, should initiate an effective warning or provide appropriate decision proposals for downstream systems or human operators to detect a potential threat early and to inform affected regions, in particular the local population, well in advance of the consequences of the event so that actions and measures to protect people, property and the environment can be made in a timely manner.
  • warning systems can address different spatial warning units within a service area or a destination region and supply them with general or individual warnings according to a determined warning level or a warning level or, if appropriate, provide individual or general decision proposals for an operator or downstream systems.
  • the known warning systems generally see different warning levels (warning level) that can trigger certain precautions or actions in the warning segment concerned.
  • the German Remote Sensing Data Center (DFD) is developing technical components of a tsunami early warning system, including the central Decision Support System (DSS), which monitors the tsunami early warning process supports and generates warning segment-specific alerts.
  • DSS central Decision Support System
  • the Major Warning (red) color code triggers significant action by local policy makers, typically evacuations.
  • warning levels are set in the known systems only on the basis of individual hazard parameters.
  • the tsunami early warning only the expected wave height on the coastline is considered in the internationally recognized systems.
  • a high intensity of a natural hazard or a technical hazard does not necessarily cause great damage to humans and the environment.
  • high intensity in one location can cause fewer victims or damage to critical infrastructure than lower natural hazard intensity elsewhere.
  • the occurrence of an earthquake in a practically deserted area called. Consequently, the specification of warning levels, which is based solely on the natural hazard intensity or derived primary criteria, can lead to disadvantageous conclusions with regard to the intensity of the damage and possibly cause adverse actions.
  • the known classification of the warnings in the known early warning systems based on the warning levels determined according to individual primary criteria is therefore possibly disadvantageous.
  • the invention is therefore based on the object of providing an apparatus and a method which improve the provision of warnings in early warning systems.
  • a device for computer-assisted assignment of predefined alert levels to spatial alert units of a target region.
  • the predefined warning levels indicate the endangerment of a region through a natural or technical hazard event.
  • the device comprises a data storage unit for storing first data defining the warning units, second data representing the spatial distribution of egg Defining properties of the target region and storing predefined alert levels.
  • the apparatus includes a database for storing deterministic models that define the spatial distribution and timing of a defined impact of a natural or technical hazard event in the target region.
  • a determination unit for determining at least one of the models of the database on the basis of input data measured when a hazard event occurs, and a processing unit for determining a hazard index for at least one of the warning units taking into account the specific models and the first and second data, and for assignment one or more of the predefined warning levels to the at least one warning unit, based at least in part on the hazard index.
  • the deterministic models include the definition of a hazard area and arrival times of the effects of the hazard events in the target region.
  • the second data comprises at least one spatial distribution of population density, density of critical infrastructure, such as hospitals or schools, age and gender distribution, topography, land cover, and access points to safe areas.
  • the determination unit and the processing unit operate dynamically in response to detecting a natural or technical hazard event concerning the target region.
  • the processing unit further considers information about uncertainty or ambiguity in the measured input data, second data or the determination of the models.
  • a method for computer-aided allocation of predefined alert levels to the spatial alert units of a target region is provided.
  • the predefined warning levels indicate the endangerment of a region through a natural or technical hazard event.
  • the method includes the steps of storing first data defining the warning units, second data defining the spatial distribution of properties of the target region, and the predefined alert levels.
  • the method comprises the storage of deterministic models, which determine the spatial distribution and the chronological sequence of a Defining a defined impact of a natural or technical hazard event in the target region, in a database, and determining at least one of the models of the database on the basis of measured input data when a hazard event occurs.
  • the method comprises determining a hazard index for at least one of the warning units taking into account the specific models and the first and second data and assigning one or more of the predefined alert levels to the at least one alert unit based at least in part on the hazard index.
  • the method further includes the steps of determining expected arrival times of the impact of a hazard event and determining expected impact intensities.
  • the method also includes determining the vulnerability of the population and the critical infrastructure based on the expected arrival times and impact levels and the second data, as well as determining the hazard index, taking into account the identified vulnerability of the population and the critical infrastructure.
  • the predefined warning levels are classified on the basis of measured input values or primary hazard parameters derived therefrom.
  • the consideration of the hazard index results in a higher or lower classification depending on the determined hazard index.
  • the hazard index is further determined by weighted sums.
  • the assignment of the at least one predefined warning level to the at least one warning unit includes the consideration of the hazard index and other criteria using weighting factors or mapping functions.
  • a computer-readable data carrier is further provided, which comprises computer-executable instructions which, when executed by a computer, perform a method for the computer-aided assignment of predefined warning levels to spatial warning units of a target region segmented into spatial warning units according to the method according to the invention.
  • the invention is based on the recognition that the effects of risk events on humans and the environment do not depend exclusively on the intensity of the risk event. hang. Further dependencies may arise, in particular, from vulnerability components that reflect vulnerabilities or the degree of adverse effects on humans and the environment.
  • a natural hazard classified at medium warning level may result in high levels of damage in an affected area due to high human and environmental vulnerabilities. Therefore, a rating of a warning level is only disadvantageous due to natural hazard intensity or derived primary criteria.
  • the objective of triggering on-the-spot action in the warning segments via a carefully selected warning level requires integrated consideration of all relevant factors, ie including, in addition to an expected level of natural or technical hazard intensity, also the results of a hazard analysis of humans and other risk factors.
  • a risk level of a spatial warning unit (warning segment) is derived, which ensures a better and more differentiated estimation of the expected effects of the natural hazard on humans and the environment.
  • the determination of a risk-based warning level can be done case-specifically and individually in a concrete warning case, is thus an online function.
  • Fig. 1 is a block diagram illustrating an inventive apparatus for use in the early warning according to an embodiment of the present invention
  • FIG. 2 is a flow chart illustrating the steps of a method for computer-assisted assignment of alert levels to spatial alert units according to an embodiment of the present invention
  • Fig. 3 is a schematic. Block diagram with cartographic representations to illustrate the determination of a potentially vulnerable surface according to embodiments of the present invention
  • 4 is a schematic cartographic illustration illustrating a possible static exposure distribution of critical infrastructure according to an example embodiment
  • FIG. 5 shows an exemplary schematic illustration for illustrating a possible static exposure distribution of the population according to an exemplary embodiment of the invention
  • Fig. 6 is a schematic representation of the illustration of the position distribution of
  • Access points to secure areas according to an exemplary embodiment of the invention
  • 7 to 1 1 are schematic representations illustrating exemplary spatially differentiated information that may be used in accordance with embodiments of the invention in the assignment of warning levels or illustrate such;
  • FIG. 2 is a schematic cartographic representation illustrating particularly vulnerable areas according to an embodiment of the invention
  • FIG. 13 is a diagram illustrating the number of affected persons as a function of time that is available for an evacuation action, according to one embodiment of the invention.
  • 15 is a schematic block diagram of the previously known warning level
  • FIG. 1 shows a schematic block diagram of a device according to the invention for computer-aided assignment of predefined warning levels to spatial warning units.
  • a target region ZR is segmented into the warning units WE1, WE2 and WE3.
  • the predefined warning levels indicate the danger to a single region as a result of a natural or technical hazard.
  • the alert levels may also be generally assigned to multiple alert segments or alert units of the target region.
  • the device 100 holds in a data storage unit 1 10 first data 1 12, which define the warning units.
  • second data 1 14, which define the spatial distribution of properties and parameters of the target region ZR, are stored therein.
  • the second data 14 may indicate the spatially resolved population density, density of critical infrastructure, for example of hospitals or schools, the spatially resolved age and gender distribution, topography, land cover or access points into safe areas.
  • the data storage unit 1 10 stores at least predefined warning levels 1 16, which are used, for example, in known early warning systems. Typically, these warning levels are classified based on measured input values or intensity primary parameters derived therefrom. That is, in general, the warning levels are classified according to an expected intensity of the present hazard event.
  • deterministic models 122 are stored which define the spatial distribution and timing of one or more defined effects of a natural or technical hazard event in the target region.
  • these deterministic models may define a hazard area and the spatially resolved arrival times of the effects of a hazard event in a target region.
  • measured values are preferably acquired by sensors which are passed on directly or after further processing as input data 135 to a determination unit 130 which determines one or more of the models of the database as relevant models on the basis of this input data 135 measured when the hazard event occurs , This determination will be described in greater detail by way of example in the following FIG.
  • the processing unit 140 taking into account the models thus determined and the first and second data, determines a hazard index for each or all of the warning units of the target region.
  • the processing unit 140 further sees the assignment of one of the predefined warning levels to the respective warning units at least partially on the basis of the hazard index determined for the warning unit. According to embodiments of the invention, several of the predefined warning levels may also be assigned to a single warning unit.
  • warning levels 145 assigned to the individual warning units WE1, WE2 and WE3 can be made available to the early-warning system or downstream systems or human operators. For example, a warning level can be assigned to a warning unit as part of a decision support system.
  • the determination unit 130 and the processing unit 140 dynamically operate, in the event of an incident, in response to the detection of a natural or technical hazard event concerning the target region. on-line.
  • the processing unit may consider information about uncertainty or ambiguity in the measured input data, the second spatial distribution data, or in determining the relevant models.
  • the device according to the invention can also be provided separately from an early warning system or integrated into such an early warning system.
  • FIG. 2 shows, in a schematic flowchart, exemplary steps according to a method 200 according to the invention for the computer-aided assignment of predefined warning levels.
  • step 210 of the method 200 basic data is stored. This can be done by keeping the basic data in a data storage unit, which allows access to the stored basic data in case of an event. Alternatively, in the event of an incident, the basic data can be read out online, ie dynamically from corresponding, always up-to-date basic data memories, and stored in a local data storage unit in unchanged or modified form.
  • the basic data comprises first data 112 (see FIG.
  • Warning units WE1, WE2 and WE3 1) defining the warning units WE1, WE2 and WE3, second data 1 14 defining the spatial distribution of properties of the target region ZR and the predefined ones Warning levels 1 16, which can indicate the endangerment of a single region as a result of a natural or technical hazard.
  • deterministic models are stored in a database.
  • the storing 220 includes keeping the deterministic models 122 in an accessible database 120, but may also include querying and local staging of relevant deterministic models in the event of an event.
  • the deterministic models 122 may be spatial distributions, i. spatially resolved data, and the timing of one or more specific effects of a natural or technical hazard event. These can be restricted to the target region or applied to the target region only in case of an incident.
  • Step 230 determines at least one model from the database based on input data 135 measured on the occurrence of a hazard event.
  • This input data may preferably be provided by sensors provided to cover the target region.
  • a hazard index is determined for at least one of the warning units taking into account the particular models and the first and second data.
  • an assignment of defined warning levels to at least one warning unit of the target region takes place based at least in part on the hazard index.
  • the determination of the hazard index may include determining the estimated expected time of arrival of the impacts determined therefrom using the relevant deterministic models) of the hazard event underlying the respective deterministic model and / or determining corresponding expected impact intensities. Based on this, the danger exposure of the population and critical infrastructure can be determined taking into account the second data, which are stored statically, for example. The determined danger exposure of the population and critical infrastructure influences the hazard index determined according to this embodiment.
  • the predefined warning levels can be classified on the basis of measured input values or expected danger intensities, which in turn can be based on corresponding measured values.
  • the consideration of the hazard index in the manner according to the invention can bring about a higher or a lower classification, especially in border regions between two warning levels.
  • the hazard index can be determined using weighted sums.
  • the allocation of the Defined warning levels to the warning units include a consideration of the hazard index using weighting factors.
  • further criteria may be included in the allocation using further weighting factors.
  • the primary criterion used in the classification of the predefined warning levels can be taken into account in the allocation of the predefined warning levels to the warning units via a weighting factor or via a mapping function in conjunction with the danger index.
  • FIGS. 3 to 15 illustrate by way of example exemplary embodiments in the area of tsunami early warning. Firstly, the determination of the danger area on land and the expected danger intensities is illustrated, then the determination of the exposure of critical infrastructure and the population under linkage with the determined danger area, the temporal accessibility of safe areas for the population and on the basis of a comparison of expected arrival times with the evacuation times, each spatially resolved, determines a degree of concern, which is taken into account in the determination of a hazard index according to embodiments of the invention.
  • the following components can be quantified and integrated, which can also be used in embodiments for other dangerous events in a suitably modified form:
  • the components can be aggregated into an exposure index that determines the impact or exposure of humans and critical infrastructure as a function of the expected time of arrival and the expected tsunami intensity per warning segment. taken into account.
  • an exposure index that determines the impact or exposure of humans and critical infrastructure as a function of the expected time of arrival and the expected tsunami intensity per warning segment. taken into account.
  • a time-dependent, online calculation of the number of expected victims can be made.
  • the determined index can then be included in the determination of the warning levels.
  • Basis can be a function that weights the influencing factors and whose output is the warning level to be determined.
  • the determination of the warning level is an online process and is thus dynamic and case-related in case of an incident.
  • a method according to the invention may comprise the following stages:
  • a possible determination of the danger area on land at respectively expected danger intensities is represented by analysis of several predicted tsunami wave propagation scenarios (database of modeled tsunami scenarios 310) using the known wave heights known in the online case in accordance with the measured values measured by the sensors on the coastline, an appropriately affected area on land is allocated. All deterministic models or possibly calculated tsunami events which may possibly correspond to the online case can be included in the calculation of the hazard area. 3 shows two possible scenarios, which are shown separately by initial steps 320 and 360, respectively.
  • those scenarios with a maximum wave height .m 3 m of class 340 or those scenarios with a maximum expected wave height> 3 m of class 380 are determined according to the maximum wave height expected on the ascertained tsunami case .
  • the location point hits are calculated for this class and the spatially resolved expected effect is determined at the respective warning level.
  • the cartographic representation 354 shows the affected, ie the potentially vulnerable, area 351 compared to the unaffected land area 353 for a tsunami of the conventional warning level "Warning Level” while the cartographic representation 394 represents the potentially vulnerable area 391 in comparison with the one affected land area 393 according to the "Major Warning Level” warning level with maximum expected wave heights> 3 m at the coastline.
  • FIG. 4 shows an exemplary static exposure distribution of critical infrastructure. From the available basic data, all objects that have an essential function or functional relevance for the company are summarized. These include facilities that have a high concentration of people in need of care (hospitals, elementary schools, old people's homes, etc.), facilities that provide an important service (airports, police, fire brigades, etc.) and facilities that can be sources of danger ( eg power plants and chemical industry). Furthermore, the determination of the exposure of critical infrastructure may include the density of transport or supply lines, such as: As roads, railways, electricity lines, oil lines or water supply lines, enter.
  • a density measure for example number of objects per unit area
  • classes that give a representative degree of exposure of critical infrastructure.
  • the critical infrastructure exposition can then be calculated according to the previously determined expected hazard area.
  • FIG. 5 shows an exemplary simplified static exposure distribution of the population according to one embodiment.
  • Population data, z For example, the number of people per unit area according to a census, satellite data or a corresponding estimate are aggregated into representative spatial units that can represent the degree of exposure.
  • the exposure of the population can then be calculated according to the expected hazard area.
  • This determination can be made, for example, as described above with reference to FIG.
  • the location of safe areas may be determined from the non-affected area determined in the previous step when determining the danger area on land.
  • Other criteria can be taken into account, which can be implemented by spatial analysis using algorithms in a Geographic Information System (GIS). The following criteria can be considered:
  • the area should connect spatially to an affected area.
  • Fig. 6 shows an exemplary cartographic representation of the spatial location of access points adjacent a potentially affected area, the area of maximum flood spread. Recording the temporal accessibility of a safe area.
  • the temporal accessibility of a safe area is essentially derived from the distance of a location to the nearest safe areas as well as the potential evacuation speed taking into account the access points. From this it is possible to calculate for each location the time that a person could need in a simplified presentation in order to be able to escape to safety.
  • Density of critical facilities such as schools and hospitals
  • population density age and gender distribution
  • topography and land cover
  • topography, land cover and the density of critical facilities have a reducing effect on the evacuation rate.
  • Population density as well as age and gender distribution are assigned characteristic velocities for empirical data for respective classes.
  • the respective spatial data in the corresponding classifications are then assigned respective evacuation rates or reduction factors (see Tables 1 to 4), which can be included in the further calculations.
  • Critical facilities such. Schools and hospitals have specific evacuation behavior. Areas with a high density of critical facilities are therefore limited in their evacuation behavior. Depending on the density of the facilities (number of facilities per hectare), the respective potential evacuation rate can be reduced (e.g., by a factor between 0 and 100 percent).
  • Table 1 shows a parameterization example on the influence of the density of critical devices on the evacuation rate.
  • PS for schools
  • KiGa for kindergartens.
  • the reduction factor depends first of all on whether hospitals are located in one area or not. With a density of hospitals per hectare equal to zero, the reduction factor results after the middle column (“no hospital”), with a hospital density greater than zero after the right column ("density> 0 hospitals / ha").
  • FIG. 7 shows by way of example a velocity distribution with regard to population density.
  • Table 3 shows a parameterization example for the influence of the slope as a reduction factor (cost) on the evacuation speed.
  • the land use or land cover can also inhibit the evacuation rate.
  • an evacuation on a road can be done faster than through dense forest.
  • This reduction effect of land cover can be taken into account by reduction factors.
  • Table 4 shows a parameterization example to take into account the influence of land cover as a reduction factor (cost) on the evacuation rate:
  • FIG. 8 An exemplary representation of the land cover-dependent reduction factors (costs) on the evacuation rate is illustrated in FIG. 8.
  • an inverse speed can now be calculated, as shown by way of example in FIG.
  • This can be used to obtain a spatially resolved quantification of the time, from an arbitrary location in the danger zone to a safe area, through an inverse distance weighting approach using, for example, a Geographical Information System (GIS).
  • GIS Geographical Information System
  • the Geographical Information System contains, for example, land use, population and topography data as well as data on critical facilities and age and gender distribution. Via so-called reclassification parameters one obtains the assigned costs.
  • FIG. 10 shows by way of example the distribution of the inverse velocity for the region underlying FIGS. 7 and 8.
  • So-called "Shelter-Basin” maps structure catchment areas of the respective safe areas and define access points to these areas, which indicate which area is in each case assigned to an access point, ie in which catchment area the respective access point is the fastest to reach Spatially varying evacuation capacity and the spatial arrangement of safe areas are illustrated by way of example in the simplified cartographic illustration of Figure 11, taking into account expected time of arrival of the effects of the hazard event
  • the expected time of arrival is known and used dynamically for further analysis. This time in minutes represents the maximum time available for the evacuation of humans at the respective times in the online case, the so-called “tsunami response time.” Since it is possible to calculate the necessary evacuation time for each landing point, it is now possible for certain time slices the average expected time of arrival is the area in which the evacuation time is insufficient.
  • the number of people in each area can now be determined in accordance with point 4 above.
  • the time course of the increase in the number of expected sacrifices for which the evacuation time will no longer be sufficient can be represented.
  • FIG. 13 shows an example diagram of the expected number of victims as a function of the tsunami response time. It represents the number of victims for whom the evacuation time will no longer be sufficient and therefore must be presumed to be at least injured by the effects of the damage event, depending on the available response time, i. the time available for an evacuation act. In the example shown, a maximum response time of 50 minutes is available. Even if this time is exhausted, about 35,000 people are affected. If no evacuation action takes place until the event occurs on land, i. if the response time is zero, 120,000 affected people can be expected in the example shown.
  • the hazard area and intensity information, population exposure and critical infrastructure information, as well as expected casualties, may be evaluated by other criteria, such as: the expected maximum wave height or arrival time to a severity measure "P" according to criteria and thresholds defined in the warning center, in some embodiments using appropriate weighting factors "G", linked to alert levels or assigned corresponding alert levels.
  • Fig. 14 shows a corresponding allocation of warning levels taking into account criteria 1 to 5 according to the invention.
  • vulnerability criteria such as the number of victims to be expected, the population exposure and the critical infrastructure
  • Fig. 15 the allocation of a warning level to a spatial warning unit takes place only on the basis of a primary criterion, in the shown example of the area of tsunami early warning according to international standards previously used scales based only on the expected wave height on the coastline.
  • Fig. 15 known predefined warning levels and their classification are shown.
  • An expected wave height on the shoreline below 10 cm does not cause a warning level to be assigned.
  • a wave height between 0.1 and 0.5 m is classified as a "minor tsunami” or “minor tsunami”, and corresponding spatial warning units are assigned a “consultation” warning level, corresponding to a wave height between 0.5 and 3 m ( Classification "Tsunami”) assigned a warning level “Warning.”
  • a wave height above 3 m the classification as "major tsunami” or “major tsunami” and the assignment of a warning level "Major Warning” or "Major Warning”.
  • the conventional classification of the warning levels can be maintained in one embodiment.
  • the risk criteria can be taken into account by changing the mapping of the value intervals to the corresponding warning levels.
  • warning level mapping can be realized by:
  • Each criterion is assigned a weighting criterion criterion (parameter list) - ⁇ R (mapping to the real numbers).
  • Each of these evaluation functions is mapped to the unit interval by means of a scaling function scal () - »[0 ... 1]. By appropriately selecting the scaling function, the relevant value ranges can be emphasized.
  • the scaled evaluation functions can be multiplied by criteria-specific weights ⁇ ⁇ ⁇ . For these, the sum over all weights gives Uj one.
  • the sum of the weighted and scaled evaluation functions gives an evaluation in the unit interval, which can be mapped to the existing warning levels by means of suitably chosen threshold values.
  • the assignment to the warning levels can also be carried out according to a primary criterion.
  • This primary criterion can be, for example, the wave height.
  • the application of further criteria may take the form of weighted premiums or discounts to the primary criterion, thereby effecting the selection of a warning level other than the primary one. For example, if a wave height of 2.8 m could first identify the warning level of the color code ⁇ orange>, then a very high exposure as a converted 0.5 m impact would ultimately result in the warning level of the color code ⁇ red> corresponding to a wave height of 3.3 m ,
  • a risk-based determination according to the invention of spatially differentiated warning levels to be applied may be e.g. in a decision support system, such as the GITEWS DSS.
  • a decision support system such as the GITEWS DSS.
  • Tsunami Decision Support System GITEWS-DSS
  • risk information can be embedded and made available to the system both in the technical determination of optimal decision proposals and in the presentation of the same.
  • Warning levels determined according to the invention can be proposed to decision makers for triggering.
  • the underlying parameters determined in the method according to the invention within the scope of the specific warning case and individually for each spatial warning segment can be displayed in a displayed table next to the assigned, preferably color-coded warning levels.

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Abstract

L'invention concerne un procédé et un dispositif d'attribution assistée par ordinateur de degrés d'avertissement prédéfinis à des unités spatiales d'avertissement dans une région cible, les degrés d'avertissement prédéfinis indiquant l'exposition d'une région à un événement dangereux naturel ou technique. Le dispositif comprend : une unité de stockage de données, destinée à stocker des premières données qui définissent les unités d'avertissement, des deuxièmes données qui définissent la distribution spatiale de propriétés de la région cible et des degrés d'avertissement prédéfinis, une base de données, destinée à stocker des modèles déterministes qui définissent la distribution spatiale et le déroulement temporel d'un effet défini d'un événement dangereux naturel ou technique dans la région cible, une unité de détermination, destinée à déterminer au moins l'un des modèles de la base de données en fonction de données d'entrée mesurées lors de la survenance d'un événement dangereux, et une unité de traitement, destinée à déterminer un indice de danger pour au moins l'une des unités d'avertissement en tenant compte des modèles déterminés et des premières et deuxièmes données et à attribuer un ou plusieurs des degrés d'avertissement prédéfinis à ladite au moins une unité d'avertissement, au moins en partie sur la base de l'indice de danger.
PCT/EP2010/007298 2009-12-11 2010-12-01 Dispositif et procédé d'attribution de degrés d'avertissement sur la base de risques WO2011069613A1 (fr)

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CN114333257A (zh) * 2021-12-30 2022-04-12 中国科学院、水利部成都山地灾害与环境研究所 一种滑坡变形速率临界值确定及滑坡预警方法
CN114519921A (zh) * 2022-02-24 2022-05-20 重庆大学 一种基于工业云的模块化滑坡监测预警系统
CN115394052A (zh) * 2022-08-30 2022-11-25 重庆地质矿产研究院 基于机器学习获得地质灾害预警关键参数预测值的方法
CN115601927A (zh) * 2022-09-19 2023-01-13 苔花科迈（西安）信息技术有限公司（Cn） 一种基于算法模型的煤矿报警事件决策方法及系统
CN116482763A (zh) * 2023-06-19 2023-07-25 浙江大学海南研究院 基于逻辑树法的概率性地震海啸灾害分析方法

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CN114333257A (zh) * 2021-12-30 2022-04-12 中国科学院、水利部成都山地灾害与环境研究所 一种滑坡变形速率临界值确定及滑坡预警方法
CN114333257B (zh) * 2021-12-30 2023-05-26 中国科学院、水利部成都山地灾害与环境研究所 一种滑坡变形速率临界值确定及滑坡预警方法
CN114519921A (zh) * 2022-02-24 2022-05-20 重庆大学 一种基于工业云的模块化滑坡监测预警系统
CN115394052A (zh) * 2022-08-30 2022-11-25 重庆地质矿产研究院 基于机器学习获得地质灾害预警关键参数预测值的方法
CN115394052B (zh) * 2022-08-30 2023-06-27 重庆地质矿产研究院 基于机器学习获得地质灾害预警关键参数预测值的方法
CN115601927A (zh) * 2022-09-19 2023-01-13 苔花科迈（西安）信息技术有限公司（Cn） 一种基于算法模型的煤矿报警事件决策方法及系统
CN116482763A (zh) * 2023-06-19 2023-07-25 浙江大学海南研究院 基于逻辑树法的概率性地震海啸灾害分析方法
CN116482763B (zh) * 2023-06-19 2023-08-25 浙江大学海南研究院 基于逻辑树法的概率性地震海啸灾害分析方法

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