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EP-4427184-B1 - AUTOMATED PARAMETRIC MODULAR SYSTEM OR DIGITAL PLATFORM FOR FORWARD-LOOKING MEASUREMENTS AND RATINGS OF MEASURABLE IMPACTS OF OCCURRING FLOOD EVENTS AND MODULAR AUTOMATED RISK-TRANSFER STRUCTURE PROVIDING AN ADJUSTABLE FLOOD IMPACT COVER AND METHOD THEREOF

EP4427184B1EP 4427184 B1EP4427184 B1EP 4427184B1EP-4427184-B1

Inventors

  • SUNDERMANN, Lukas

Dates

Publication Date
20260506
Application Date
20221102

Claims (20)

  1. An automated method for providing a dynamic parametric flood impact cover (1031) for physical objects (3/31) being measurably impacted by an occurrence (41/42/43) of a flood event (4) by using an adaptive damage-cover structure (1041) of a damage-cover system (104) based on physical flood event measurements, wherein a central measuring engine (10) captures a geographic area (2) to be covered by a predefined data structure (1005), the data structure (1005) at least comprising definable area parameters (10051) capturing geographic location (100511) and geographic extent (100512) of said geographic area (2), comprising: splitting, by the central measuring engine (10), the geographic area (2) to be covered by a spatial grid (1002) with adjustable grid cells (1003) of definable size (10033), wherein the cells (1003) are dynamically adapted by applying areas closer to a flooding and/or river with a higher measuring resolution than areas further away, and wherein a grid cells (1003) is detected to be a flooded grid cell or a not flooded grid cell by the grid cell measurements, detecting grid cells (1003) to be measured as flooded by a machine-learning based engine with the machine-learning structure providing a grid-cell-based floodplain mapping as a remote sensing process over the defined geographic area (2), wherein by a digital elevation modelling structure of the machine-learning based engine a grid-cell-based floodplain mapping is provided, and wherein slope and terrain characteristics of the selected area are captured by the digital elevation modelling structure with an adaptive measuring accuracy based on the selected grid size, subdividing the spatial grid (1002) into cell-squared grids, associating for each of the grids its own machine-learning structure with an input and an output layer, and inputting measurements of real-time discharges of the upstream catchments or flood level measurements, for a prediction of a maximum flood inundation as output of the machine-learning structure for a grid cell (1003), wherein the number of inputs to the input layer of a machine-learning structure is based on the number of hydrographs within said grid cell (1003) providing a discharge or flood measuring curve, and wherein the output of the output layer providing a flood inundation extent in each grid cell (1003), measuring, by flood detection devices and/or flood sensors (102), an occurrence of a flood event (2) by measuring floodings within the grid cells (1003) of the spatial grid (1002), wherein flood measuring parameters (1021) of the flood detection devices and/or flood sensors (102) are transmitted to the central measuring engine (10) and wherein, based on the transmitted flood measuring parameters (1021), grid cells (1003) measured as flooded are contributing to the area measured as affected (21) while grid cells measured as not flooded are contributing to the area measured as not affected (22); measuring a flood threshold measurand, wherein the flood threshold measurand (1032) is selected from a percentage (23) of the geographic area (2) given by the area measured as affected (21) to the total geographic area (2); and triggering the adaptive damage-cover structure (1041) to cover physical damages or losses (32) associated with the measured occurrence of the flood event (4), the occurrence measurably impacting physical objects (3) situated in the grid cells (10031) measured as affected, wherein the adaptive damage-cover structure (1041) is adapted by the damage-cover system (104) based on the flood threshold measurand (1032) providing the dynamic parametric flood impact cover (1031).
  2. Method according to claim 1, characterized in that the adaptive damage-cover structure (1041) is only triggered if the flood threshold measurand (1032) is measured to exceed a predefined flood threshold value (1033).
  3. Method according to claim 1, characterized in that the adaptive damage-cover structure (1041) is triggered to provide a damage cover (1031) directly dependent on the measured flood threshold measurand (1032).
  4. Method according to one of the claims 1 to 3, characterized in that the adaptive damage-cover structure (1041) of the damage-cover system (104) is triggered by transmitting electronic steering signals from to the automatedly steered electronic first and/or second resource-pooling system (1042/1043), wherein the adaptive damage-cover structure (1041) is adapted by the damage-cover system (104) based on the flood threshold measurand (1032) transmitted by the electronic steering signals.
  5. Method according to one of the claims 1 to 3, characterized in that the triggering the adaptive damage-cover structure (1041) for covering physical damages or loss (32) associated with the measured occurrence of the flood event (4) comprises generating a parametric coverage (1031) based on the adaptive damage-cover structure (1041), and transferring, by an electronic payment transfer module, based on the generated parametric coverage (1031) monetary pay-out parameter values by electronic payment transfer to cover the physical damages or loss (32) associated with the measured occurrence.
  6. Method according to one of the claims 1 to 5, characterized in that the grid cells (1003) are defined as two dimensional blocks of m x n size (10033).
  7. Method according to claim 6, characterized in that the two dimensional m x n blocks (1003) are of approx. 8.72 * 10-5 radian size (10033).
  8. Method according to claim 6, characterized in that the two dimensional grid 1002 of 0.0005 is 8.72 * 10-5 radian size (10033).
  9. Method according to claim 6, characterized in that the dimensional m x n blocks (1003) are of approx. 0.005 * 0.005 deg size (10033).
  10. Method according to one of the claims 1 to 9, characterized in that the geographic area (2) comprising surveyed landscape representative of area covered by dry land (24) and wetland (25).
  11. Method according to one of the claims 1 to 10, characterized in that the occurrence of the flood event (4) is detected using loopback signaling (1022).
  12. Method according to one of the claims 1 to 11, characterized in that the grid cells (1003) measured as flooded (10031) are determined using at least an artificial intelligence based data-processing structure and/or a machine-learning-based data-processing structure.
  13. Method according to one of the claims 1 to 12, characterized in that the affected area is measured using at least partially air-based and/or space-based optical measuring devices (1023/1024).
  14. Method according to one of the claims 1 to 13, characterized by generating a premium value based on a specific selection of the parametric flood impact cover (1031).
  15. Method according to one of the claims 1 to 14, characterized in that the triggering the adaptive damage-cover structure (1041) for covering physical damages or loss (32) associated with the measured occurrence of the flood event (4) comprises generating a parametric coverage (1031) based on a cover activation signaling or electronic payout function (1046), and transferring, by an electronic payment transfer module, based on the output of the cover activation signaling or electronic payout function (1046)a monetary pay-out by electronic payment transfer to cover the physical damages or loss (32) associated with the measured occurrence.
  16. Method according to claim 15, characterized in that the cover activation signaling or electronic payout function (1046) comprises a linear payout function (10461) and/or a stepped payout function (10462) and/or a deductibles-based payout function (10463).
  17. Method according to one of the claims 15 or 16, characterized in that the cover activation signaling or electronic payout function (1046) is selectable based on topography and/or exposure distribution.
  18. Method according to one of the claims 15 to 17, characterized in that the cover activation signaling or electronic payout function (1046) is user-specific definable.
  19. Method according to one of the claims 15 to 18, characterized in that the geographic area (2) comprises at least parts definable as automatically excluded from the dynamic parametric flood impact cover (1031).
  20. A flood measuring and trigger system (1) for triggering a dynamic parametric flood impact cover for physical objects (3/31) being measurably impacted by an occurrence of a flood event (4) by using an adaptive damage-cover structure (1031) of an electronic damage-signaling and/or damage-cover system (103) based on physical flood event measurements, wherein the flood measuring and trigger system (1) comprises a central measuring engine (10) with a predefined data structure (1005) for capturing a geographic area (2) to be covered, the data structure (1005) at least comprising definable area parameters (10041) capturing geographic location (100511) and geographic extent (100512) of said geographic area (2), comprising: in that predefined data structure (1005) comprises grid parameters (10033) for splitting the geographic area (2) to be covered by the spatial grid (1002) with variable grid cells (1003) of automatically adaptable size (10033) by means of a dynamic grid splitter (1001), wherein the cells (1003) are dynamically adapted with areas closer to a flooding and/or river applying with a high measuring resolution than areas further away, and wherein a grid cells (1003) is detected to be a flooded grid cell or a not flooded grid cell by the grid cell measurements, in that the flood measuring and trigger system (1) comprises a machine-learning based engine with the machine-learning structure for detecting grid cells to be measured as flooded by a grid-cell-based floodplain mapping as a remote sensing process over the defined geographic area (2), wherein by a digital elevation modelling structure of the machine-learning based engine a grid-cell-based floodplain mapping is provided, and wherein slope and terrain characteristics of the selected area are captured by the digital elevation modelling structure with an adaptive measuring accuracy based on the selected grid size, in that the spatial grid (1002) is subdivided into cell-squared grids, each of the grids having an own machine-learning structure associated with an input and an output layer, wherein, for a prediction of a maximum flood inundation as output of the machine-learning structure for a grid cell (1003), measurements of real-time discharges of the upstream catchments or flood level measurements are inputted, wherein the number of inputs to the input layer of a machine-learning structure is based on the number of hydrographs within said grid cell (1003) providing a discharge or flood measuring curve, and wherein the output of the output layer providing a flood inundation extent in each grid cell (1003), in that the flood measuring and trigger system (1) comprises flood detection devices and/or flood sensors (102) for measuring an occurrence of a flood event (4) by measuring floodings within grid cells (1003) of the spatial grid (1002), wherein flood measuring parameters (1021) of the flood detection devices and/or flood sensors (102) are transmitted to the central measuring engine (10) and wherein, based on the transmitted flood measuring parameters (1021), grid cells (1003) measured as flooded are contributing to the area (21) measured as affected while grid cells measured as not flooded are not contributing to the area (22) measured as affected; in that the central measuring engine (10) comprises a flood threshold measurand (1032), wherein the flood threshold measurand (1032) is selected from a percentage (23) of the geographic area (2) given by the area (21) measured as affected to the total geographic area (2), and in that the central measuring engine (10) comprises an electronic flood trigger (103) for triggering the adaptive damage-cover structure (1041) covering physical damages or loss associated with the measured occurrence of the flood event (4), the occurrence measurably impacting physical objects (3/31) situated in the grid cells (1011) measured as affected (10111), wherein the adaptive damage-cover structure (1041) is adapted by the damage-cover system (104) based on the flood threshold measurand (1032) providing the dynamic parametric flood impact cover (1031).

Description

Field of the invention The field of the invention is directed towards automated systems providing parametric flood event impact cover to one or more objects based on forward-looking and/or predictive measurements of occurrences and occurrence rates of measurable physical impacts of catastrophic events, in particular measurably impacting flood events. The measurements related to measurands depending on the physical, location-dependent event strength, location (as geographic area-based, cell-based, event strength line based, or geographic or topographic coordinate based (as latitude and longitude)), and measured time window, in particular to measurable impacts associated with the occurrence of flood events. Further, the invention is directed to automated parametric mitigation or transfer of the measured forward-looking impact to a specific object by an automated risk-transfer or risk-absorption system, where the impact e.g. is measured in units of expected damage rate, percentage or other quantifying and/or measuring units associated with the measured forward-looking impact the specific object. This invention further relates to automated methods and systems for automated location-dependent recognition of flood occurrence probabilities (denoted as flood risks or flood hazards), where flood states are automatically measured or captured, and location-dependent forward-looking probability values for flood hazards are automatically forecasted (e.g. by stochastic structures or machine-learning structures), measured or generated based on the direct measuring link to the physical environment. Finally, the invention relates to digital, modular platforms for automated mitigation of impacted physical damages to physical objects on a certain geographic location and future time window. Background of the invention Today reliable automated flood impact and impact response or mitigation systems are painfully lacking. For many countries, it is hardly possible to do a technically correct flood impact occurrence rating and/or determination based on predictive forward-looking impact measures. A glance at the loss history shows that physical damages and associated losses caused by flood events are equally high or higher than those of other natural catastrophic events as earthquakes, windstorms, or other perils. For many of those other perils various prediction and/or rating and/or early warning systems based on actual measuring parameter values already exist. Large physical part of industrial facilities, industrial power and time are lost by occurring flood events having a physical impact to such objects. Additionally, with the trend of increasing risk-transfer penetration for floods, the insurance and re-insurance industry is affected ever more by hardly to measure and predict flood events causing physical damages and losses. To extend the early warning capabilities and flood damage cover, however, the threat of immense data amounts has to be coped with. One new approach is the present inventive measuring system, inter alia, allowing to extrapolate the actual physical measuring parameters to future time windows and geographic cells. Further, in many countries, a large number of industrial facilities and homes have a significant and measurably predictable probability (risk) for being impacted by flood events, and reasonably should be covered by flood mitigation and risk-transfer processes. However, many prior art systems are not capable to reliably hedge against the technically difficult to predict perils of flood events, inter alia, due to the prevalence of moral hazard and adverse selection phenomena, for example, in entering risk-transfers for objects most affected by the specific peril of flood. In such cases, traditional risk-transfer is not available. Whereas for other damage risks, risk-transfer systems can be based on the use of the law of large numbers to precisely determine a relatively small premium amount to large numbers of objects in order to cover the occurring damages of the small numbers of impacted objects who have suffered a loss due to the event-based impact to their objects. In flood event covers, typically the numbers of impacted objects is larger than the available number of individuals interested in protecting their property/objects from the peril using risk covers, which means that most prior art insurance systems do not provide risk-transfers to occurring flood events since the probability of operating the system in a sound profit range are regarded as being remote. Additionally, while there are risk-transfer systems that are enabled to provide primary flood risk covers for high value homes, the underwriting and provision of such mitigation processes does not account for many flood risks. The lack of flood reliably automatable risk mitigation, risk-transfer and risk covering systems can be detrimental to the operation of many industrial facilities at a certain location or detrimental to local homeowners