KalmanFilterinRealTimeURBIS卡爾曼濾波器在實(shí)時(shí)雅邦規(guī)劃的設(shè)計(jì)課件

1、Kalman Filter in Real Time URBISRichard Kranenburg06-01-2019第1頁(yè)，共28頁(yè)。IntroductionTNO Technisch Natuurwetenschappelijk OnderzoekKerngebied Bouw en OndergrondBusiness Unit Milieu en LeefomgevingIntroduction - Uncertainty analysis - Kalman filter - Application on population第2頁(yè)，共28頁(yè)。IntroductionAccompan

2、istsMichiel Roemer (TNO)Jan Duyzer (TNO)Arjo Segers (TNO)Kees Vuik (TUDelft)Introduction - Uncertainty analysis - Kalman filter - Application on population第3頁(yè)，共28頁(yè)。ProblemCurrent situationReal Time URBIS model gives one value for the concentration NOx in the DCMR areaWanted situationUncertainty inte

3、rval for the concentration NOxUncertainty interval dependent of the place in the domainIntroduction - Uncertainty analysis - Kalman filter - Application on population第4頁(yè)，共28頁(yè)。DCMR-areaIntroduction - Uncertainty analysis - Kalman filter - Application on population第5頁(yè)，共28頁(yè)。URBIS model11 emission sourc

4、esTrafficCARZone CardsRoad nearRoad farBackgroundAbroadRest of the NetherlandsDCMR-areaShippingShip seaShip inlandIndustryIndustryRestWinddirectionsNorthEastSouthWestWind speeds1.5 m/s5.5 m/sTotal 88 standard concentration fields for the concentration NOxIntroduction - Uncertainty analysis - Kalman

5、filter - Application on population第6頁(yè)，共28頁(yè)。Real Time URBISGives for every hour an expected concentration NOx for the whole DCMR-area, based on input parametersWind direction ()Wind speed (v)Temperature (T)Month (m)Weekday (d)Hour (h)State equation:Introduction - Uncertainty analysis - Kalman filter

6、- Application on population第7頁(yè)，共28頁(yè)。Measurement locationsDCMR-StationsSchiedamHoogvlietMaassluisOverschieRidderkerkRotterdam NoordRIVM-StationsSchiedamsevestVlaardingenBentinckpleinIntroduction - Uncertainty analysis - Kalman filter - Application on population第8頁(yè)，共28頁(yè)。Uncertainty Real Time URBISComp

7、are Real Time URBIS simulations with the observations on the nine measurement locationsBoth observations and model simulations have a log-normal distributionIntroduction - Uncertainty analysis - Kalman filter - Application on population第9頁(yè)，共28頁(yè)。Log-normal distributionsIntroduction - Uncertainty anal

8、ysis - Kalman filter - Application on population第10頁(yè)，共28頁(yè)。Correction of the modelDifferences between model and measurements plotted with respect to 6 input parameters h: hour of the day: wind directionIntroduction - Uncertainty analysis - Kalman filter - Application on population第11頁(yè)，共28頁(yè)。Uncertaint

9、y of the modelStandard deviation of the differences between the corrected model and the observations v: wind speedIntroduction - Uncertainty analysis - Kalman filter - Application on population第12頁(yè)，共28頁(yè)。Result of uncertainty analysisIntroduction - Uncertainty analysis - Kalman filter - Application o

10、n population第13頁(yè)，共28頁(yè)。Kalman filterSmooth random errors in the model of a dynamical systemIn a real time application, measurements on time k are directly available to filter the state on time k. Two results after applicationNew expected concentration NOxUncertainty interval for the concentration NOx

11、 New state equation:Introduction - Uncertainty analysis - Kalman filter - Application on population第14頁(yè)，共28頁(yè)。Kalman filter equationsForecast Analysis Introduction - Uncertainty analysis - Kalman filter - Application on population第15頁(yè)，共28頁(yè)。Kalman filter on emission source BackgroundIn the vector all

12、values are equal to zero, except the entries corresponding with the source background Linearization of the Kalman filter equationsMatrix A estimated with measurements in Schipluiden and WestmaasMatrix R estimated with measurements at BentinckpleinIntroduction - Uncertainty analysis - Kalman filter -

13、 Application on population第16頁(yè)，共28頁(yè)。Kalman filter on emission source BackgroundScreening criterion:Pfabs,k : Model uncertainty after forecast stepRabs,k : Uncertainty of the measurementsIntroduction - Uncertainty analysis - Kalman filter - Application on population第17頁(yè)，共28頁(yè)。Kalman filter on emission

14、 source Background Introduction - Uncertainty analysis - Kalman filter - Application on population第18頁(yè)，共28頁(yè)。Kalman filter on all emission sourcesState equation:Screening CriterionIntroduction - Uncertainty analysis - Kalman filter - Application on population第19頁(yè)，共28頁(yè)。Kalman filter on all emission so

15、urcesMatrix A estimated with measurements in Schipluiden, Westmaas, Overschie, Ridderkerk, Maassluis, VlaardingenMatrix R estimated with measurements at BentinckpleinIntroduction - Uncertainty analysis - Kalman filter - Application on population第20頁(yè)，共28頁(yè)。Kalman filter on all emission sourcesIntroduc

16、tion - Uncertainty analysis - Kalman filter - Application on population第21頁(yè)，共28頁(yè)。Connection with populationEach grid cell, every hour a certainty intervalAnnual mean of the width of these intervals per grid cellAmount of large widths of these intervals per grid cellNumber of postal zipcodes per grid

17、 cellPopulation density 1.99 people per grid cellNumber of people per grid cellIntroduction - Uncertainty analysis - Kalman filter - Application on population第22頁(yè)，共28頁(yè)。Connection with populationIntroduction - Uncertainty analysis - Kalman filter - Application on population第23頁(yè)，共28頁(yè)。Connection with p

18、opulationIntroduction - Uncertainty analysis - Kalman filter - Application on population第24頁(yè)，共28頁(yè)。Connection with populationIntroduction - Uncertainty analysis - Kalman filter - Application on population第25頁(yè)，共28頁(yè)。Connection with populationIntroduction - Uncertainty analysis - Kalman filter - Application on population第26頁(yè)，共28頁(yè)。ConclusionsThe differences between the observations and the model simulation are not only caused by inaccuracies in the backgroundUncertainty interval has large width in the industrial region around Pernis and on the main roadsThe application of the Kal