Applications of machine learning and open geospatial data in flood risk modelling
Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Brill, Fabio Alexander [verfasserIn] Merz, Bruno [akademischer betreuerIn] Kreibich, Heidi - 1969- [akademischer betreuerIn] Schumann, Guy [akademischer betreuerIn] |
|---|---|
| Körperschaften: |
Universität Potsdam [Grad-verleihende Institution] |
| Hochschulschrift: |
Dissertation ; Universität Potsdam ; 2022 |
| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Potsdam: 2022 |
|---|
| Schlagwörter: |
Hochwasser / Hochwasserschaden / Hochwasservorhersage / Hochwasserwelle / Prognose / Katastrophenrisiko Überflutung / Überschwemmung / Risikoanalyse / Überschwemmungsgefahr / Modellierung / Maschinelles Lernen Angewandte Hydrologie / Einzugsgebiet / Überschwemmungsgebiet / Fernerkundung / Kartierung / Geoinformatik Geostatistik / Big Data / Data Mining / Datenanalyse / Deep learning / Mustererkennung Wald / Stadtregion / Datenauswertung / Open Data / Raumdaten / Überschwemmungsgefahr |
|---|---|
| Formangabe: |
Hochschulschrift |
| Anmerkung: |
Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 |
|---|---|
| Umfang: |
1 Online-Ressource (xix, 124 Seiten, 17891 KB) ; Illustrationen, Diagramme |
| Weitere Ausgabe: |
Erscheint auch als Druck-Ausgabe Brill, Fabio Alexander: Applications of machine learning and open geospatial data in flood risk modelling - Potsdam, 2022 |
|---|
| Links: |
Link aufrufen |
|---|
| DOI / URN: |
urn:nbn:de:kobv:517-opus4-555943 10.25932/publishup-55594 |
|---|
| Katalog-ID: |
1813744173 |
|---|
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| 245 | 1 | 0 | |a Applications of machine learning and open geospatial data in flood risk modelling |c by Fabio Alexander Brill |
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| 502 | |b Dissertation |c Universität Potsdam |d 2022 | ||
| 520 | |a Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] | ||
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applications of machine learning and open geospatial data in flood risk modelling |
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Applications of machine learning and open geospatial data in flood risk modelling |
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Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 |
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Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 |
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Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 |
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urn:nbn:de:kobv:517-opus4-555943 urn 10.25932/publishup-55594 doi (DE-627)1813744173 (DE-599)KXP1813744173 (OCoLC)1339090366 DE-627 ger DE-627 rda eng XA-DE-BB 551.489 DE-101 550 DE-101 Brill, Fabio Alexander verfasserin (DE-588)1264926332 (DE-627)181376610X aut Applications of machine learning and open geospatial data in flood risk modelling by Fabio Alexander Brill Potsdam 2022 1 Online-Ressource (xix, 124 Seiten, 17891 KB) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 Dissertation Universität Potsdam 2022 Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4025289-9 (DE-627)104141689 (DE-576)208959963 Hochwasser gnd s (DE-588)4257648-9 (DE-627)104671289 (DE-576)210565691 Hochwasserschaden gnd s (DE-588)4220751-4 (DE-627)105015490 (DE-576)210267461 Hochwasservorhersage gnd s (DE-588)4160306-0 (DE-627)10547875X (DE-576)209856742 Hochwasserwelle gnd s (DE-588)4047390-9 (DE-627)106194712 (DE-576)209073446 Prognose gnd s (DE-588)4202680-5 (DE-627)10515668X (DE-576)210150270 Katastrophenrisiko gnd (DE-627) s (DE-588)4293487-4 (DE-627)104193352 (DE-576)210952911 Überflutung gnd s (DE-588)4186634-4 (DE-627)104196092 (DE-576)210040432 Überschwemmung gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4453039-0 (DE-627)227482166 (DE-576)212592750 Überschwemmungsgefahr gnd s (DE-588)4170297-9 (DE-627)105403466 (DE-576)209929170 Modellierung gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) s (DE-588)4142440-2 (DE-627)105613169 (DE-576)209718781 Angewandte Hydrologie gnd s (DE-588)4151469-5 (DE-627)105545570 (DE-576)209788429 Einzugsgebiet gnd s (DE-588)4443995-7 (DE-627)225305534 (DE-576)212501186 Überschwemmungsgebiet gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4029803-6 (DE-627)106272780 (DE-576)208984267 Kartierung gnd s (DE-588)7571300-7 (DE-627)529027003 (DE-576)265408997 Geoinformatik gnd (DE-627) s (DE-588)4020279-3 (DE-627)106318640 (DE-576)20893264X Geostatistik gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd s (DE-588)4428654-5 (DE-627)216935180 (DE-576)212347217 Data Mining gnd s (DE-588)4123037-1 (DE-627)105758051 (DE-576)209556331 Datenanalyse gnd s (DE-588)1135597375 (DE-627)890512922 (DE-576)489847412 Deep learning gnd s (DE-588)4040936-3 (DE-627)104360054 (DE-576)209042761 Mustererkennung gnd (DE-627) s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4056763-1 (DE-627)106153803 (DE-576)209118865 Stadtregion gnd s (DE-588)4131193-0 (DE-627)105696773 (DE-576)20962535X Datenauswertung gnd s (DE-588)1064023886 (DE-627)812870778 (DE-576)423564129 Open Data gnd s (DE-588)4206012-6 (DE-627)105131199 (DE-576)210172576 Raumdaten gnd s (DE-588)4453039-0 (DE-627)227482166 (DE-576)212592750 Überschwemmungsgefahr gnd (DE-627) Merz, Bruno akademischer betreuerin (DE-588)114734348 (DE-627)512977178 (DE-576)175880638 dgs Kreibich, Heidi 1969- akademischer betreuerin (DE-588)124201768 (DE-627)085703559 (DE-576)294067191 dgs Schumann, Guy akademischer betreuerin (DE-588)1171146396 (DE-627)1040388752 (DE-576)51360894X dgs Universität Potsdam Grad-verleihende Institution (DE-588)2120681-8 (DE-627)101653735 (DE-576)193975041 dgg Potsdam (DE-588)4046948-7 (DE-627)106196332 (DE-576)209071575 uvp Erscheint auch als Druck-Ausgabe Brill, Fabio Alexander Applications of machine learning and open geospatial data in flood risk modelling Potsdam, 2022 xix, 124 Seiten (DE-627)181374436X http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 Resolving-System kostenfrei https://doi.org/10.25932/publishup-55594 2022-09-07 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-555943 2022-09-07 Resolving-System https://d-nb.info/1264210000/34 2022-09-07 Langzeitarchivierung Nationalbibliothek https://publishup.uni-potsdam.de/frontdoor/index/index/docId/55594 application/pdf 2022-09-07 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.489 20 01 0084 4185822146 x 08-09-22 21 01 0046 4185840446 z 08-09-22 22 01 0018 4185858361 SUBolrd xu 08-09-22 23 01 0830 4185876130 olr-d x 08-09-22 30 01 0104 4185889712 z 08-09-22 40 01 0007 4185901666 xsn 08-09-22 60 01 0705 4185919735 OLRD z 08-09-22 63 01 3401 4185936079 ORD x 08-09-22 70 01 0089 4185946120 zdo 08-09-22 105 01 0841 4186088225 z 08-09-22 110 01 3110 4185959451 x 08-09-22 132 01 0959 4185972423 OLR-DISS x 08-09-22 151 01 0546 4185987889 OLR-ODISS z 08-09-22 161 01 0960 4185995652 ORD z 08-09-22 285 01 0517 4176888504 00 9300 --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 08-08-22 293 01 3293 4186074747 ORD z 08-09-22 370 01 4370 4186084459 x 08-09-22 2027 01 DE-105 4178481568 00 --%%-- Dn --%%-- --%%-- l01 11-08-22 2403 01 DE-LFER 4192523515 00 --%%-- --%%-- n --%%-- l01 27-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 285 01 0517 https://doi.org/10.25932/publishup-55594 LF 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 2403 01 DE-LFER https://doi.org/10.25932/publishup-55594 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 2027 01 DE-105 00 s ebook 60 01 0705 10 ho 285 00 DE-517 00 UT 4700 285 00 DE-517 00 RB 10357 285 00 DE-517 00 AR 141200 285 00 DE-517 00 ST 630 2027 01 DE-105 00 (DE-627)1293399981 DK 556.166 2027 01 DE-105 00 (DE-627)1294104160 DK 556.535 2027 01 DE-105 00 (DE-627)1301491624 DK 556.06 2027 01 DE-105 00 (DE-627)1293969826 DK 556.51 2027 01 DE-105 00 (DE-627)1294075705 QQ 645 2027 01 DE-105 00 (DE-627)1294175548 DK 502.58 2027 01 DE-105 00 (DE-627)1293812447 DK 55.001.57 2027 01 DE-105 00 (DE-627)1292244216 DK 550.8.05 2027 01 DE-105 00 (DE-627)1292433272 DK 528.8 2027 01 DE-105 00 (DE-627)1293783749 DK 528.94 2027 01 DE-105 00 (DE-627)129278105X DK 378.245 2027 01 DE-105 00 (DE-627)1292263644 DK 519.25 2027 01 DE-105 00 (DE-627)1297458974 DK 630.11 2027 01 DE-105 00 (DE-627)1300971010 DK 556.04 2027 01 DE-105 00 (DE-627)1292192038 DK 519.711 2027 01 DE-105 00 (DE-627)1293391980 DK 51-7 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-09-08:14:29:25 |
| allfields_unstemmed |
urn:nbn:de:kobv:517-opus4-555943 urn 10.25932/publishup-55594 doi (DE-627)1813744173 (DE-599)KXP1813744173 (OCoLC)1339090366 DE-627 ger DE-627 rda eng XA-DE-BB 551.489 DE-101 550 DE-101 Brill, Fabio Alexander verfasserin (DE-588)1264926332 (DE-627)181376610X aut Applications of machine learning and open geospatial data in flood risk modelling by Fabio Alexander Brill Potsdam 2022 1 Online-Ressource (xix, 124 Seiten, 17891 KB) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 Dissertation Universität Potsdam 2022 Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4025289-9 (DE-627)104141689 (DE-576)208959963 Hochwasser gnd s (DE-588)4257648-9 (DE-627)104671289 (DE-576)210565691 Hochwasserschaden gnd s (DE-588)4220751-4 (DE-627)105015490 (DE-576)210267461 Hochwasservorhersage gnd s (DE-588)4160306-0 (DE-627)10547875X (DE-576)209856742 Hochwasserwelle gnd s (DE-588)4047390-9 (DE-627)106194712 (DE-576)209073446 Prognose gnd s (DE-588)4202680-5 (DE-627)10515668X (DE-576)210150270 Katastrophenrisiko gnd (DE-627) s (DE-588)4293487-4 (DE-627)104193352 (DE-576)210952911 Überflutung gnd s (DE-588)4186634-4 (DE-627)104196092 (DE-576)210040432 Überschwemmung gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4453039-0 (DE-627)227482166 (DE-576)212592750 Überschwemmungsgefahr gnd s (DE-588)4170297-9 (DE-627)105403466 (DE-576)209929170 Modellierung gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) s (DE-588)4142440-2 (DE-627)105613169 (DE-576)209718781 Angewandte Hydrologie gnd s (DE-588)4151469-5 (DE-627)105545570 (DE-576)209788429 Einzugsgebiet gnd s (DE-588)4443995-7 (DE-627)225305534 (DE-576)212501186 Überschwemmungsgebiet gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4029803-6 (DE-627)106272780 (DE-576)208984267 Kartierung gnd s (DE-588)7571300-7 (DE-627)529027003 (DE-576)265408997 Geoinformatik gnd (DE-627) s (DE-588)4020279-3 (DE-627)106318640 (DE-576)20893264X Geostatistik gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd s (DE-588)4428654-5 (DE-627)216935180 (DE-576)212347217 Data Mining gnd s (DE-588)4123037-1 (DE-627)105758051 (DE-576)209556331 Datenanalyse gnd s (DE-588)1135597375 (DE-627)890512922 (DE-576)489847412 Deep learning gnd s (DE-588)4040936-3 (DE-627)104360054 (DE-576)209042761 Mustererkennung gnd (DE-627) s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4056763-1 (DE-627)106153803 (DE-576)209118865 Stadtregion gnd s (DE-588)4131193-0 (DE-627)105696773 (DE-576)20962535X Datenauswertung gnd s (DE-588)1064023886 (DE-627)812870778 (DE-576)423564129 Open Data gnd s (DE-588)4206012-6 (DE-627)105131199 (DE-576)210172576 Raumdaten gnd s (DE-588)4453039-0 (DE-627)227482166 (DE-576)212592750 Überschwemmungsgefahr gnd (DE-627) Merz, Bruno akademischer betreuerin (DE-588)114734348 (DE-627)512977178 (DE-576)175880638 dgs Kreibich, Heidi 1969- akademischer betreuerin (DE-588)124201768 (DE-627)085703559 (DE-576)294067191 dgs Schumann, Guy akademischer betreuerin (DE-588)1171146396 (DE-627)1040388752 (DE-576)51360894X dgs Universität Potsdam Grad-verleihende Institution (DE-588)2120681-8 (DE-627)101653735 (DE-576)193975041 dgg Potsdam (DE-588)4046948-7 (DE-627)106196332 (DE-576)209071575 uvp Erscheint auch als Druck-Ausgabe Brill, Fabio Alexander Applications of machine learning and open geospatial data in flood risk modelling Potsdam, 2022 xix, 124 Seiten (DE-627)181374436X http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 Resolving-System kostenfrei https://doi.org/10.25932/publishup-55594 2022-09-07 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-555943 2022-09-07 Resolving-System https://d-nb.info/1264210000/34 2022-09-07 Langzeitarchivierung Nationalbibliothek https://publishup.uni-potsdam.de/frontdoor/index/index/docId/55594 application/pdf 2022-09-07 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.489 20 01 0084 4185822146 x 08-09-22 21 01 0046 4185840446 z 08-09-22 22 01 0018 4185858361 SUBolrd xu 08-09-22 23 01 0830 4185876130 olr-d x 08-09-22 30 01 0104 4185889712 z 08-09-22 40 01 0007 4185901666 xsn 08-09-22 60 01 0705 4185919735 OLRD z 08-09-22 63 01 3401 4185936079 ORD x 08-09-22 70 01 0089 4185946120 zdo 08-09-22 105 01 0841 4186088225 z 08-09-22 110 01 3110 4185959451 x 08-09-22 132 01 0959 4185972423 OLR-DISS x 08-09-22 151 01 0546 4185987889 OLR-ODISS z 08-09-22 161 01 0960 4185995652 ORD z 08-09-22 285 01 0517 4176888504 00 9300 --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 08-08-22 293 01 3293 4186074747 ORD z 08-09-22 370 01 4370 4186084459 x 08-09-22 2027 01 DE-105 4178481568 00 --%%-- Dn --%%-- --%%-- l01 11-08-22 2403 01 DE-LFER 4192523515 00 --%%-- --%%-- n --%%-- l01 27-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 285 01 0517 https://doi.org/10.25932/publishup-55594 LF 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 2403 01 DE-LFER https://doi.org/10.25932/publishup-55594 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 2027 01 DE-105 00 s ebook 60 01 0705 10 ho 285 00 DE-517 00 UT 4700 285 00 DE-517 00 RB 10357 285 00 DE-517 00 AR 141200 285 00 DE-517 00 ST 630 2027 01 DE-105 00 (DE-627)1293399981 DK 556.166 2027 01 DE-105 00 (DE-627)1294104160 DK 556.535 2027 01 DE-105 00 (DE-627)1301491624 DK 556.06 2027 01 DE-105 00 (DE-627)1293969826 DK 556.51 2027 01 DE-105 00 (DE-627)1294075705 QQ 645 2027 01 DE-105 00 (DE-627)1294175548 DK 502.58 2027 01 DE-105 00 (DE-627)1293812447 DK 55.001.57 2027 01 DE-105 00 (DE-627)1292244216 DK 550.8.05 2027 01 DE-105 00 (DE-627)1292433272 DK 528.8 2027 01 DE-105 00 (DE-627)1293783749 DK 528.94 2027 01 DE-105 00 (DE-627)129278105X DK 378.245 2027 01 DE-105 00 (DE-627)1292263644 DK 519.25 2027 01 DE-105 00 (DE-627)1297458974 DK 630.11 2027 01 DE-105 00 (DE-627)1300971010 DK 556.04 2027 01 DE-105 00 (DE-627)1292192038 DK 519.711 2027 01 DE-105 00 (DE-627)1293391980 DK 51-7 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-09-08:14:29:25 |
| allfieldsGer |
urn:nbn:de:kobv:517-opus4-555943 urn 10.25932/publishup-55594 doi (DE-627)1813744173 (DE-599)KXP1813744173 (OCoLC)1339090366 DE-627 ger DE-627 rda eng XA-DE-BB 551.489 DE-101 550 DE-101 Brill, Fabio Alexander verfasserin (DE-588)1264926332 (DE-627)181376610X aut Applications of machine learning and open geospatial data in flood risk modelling by Fabio Alexander Brill Potsdam 2022 1 Online-Ressource (xix, 124 Seiten, 17891 KB) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 Dissertation Universität Potsdam 2022 Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. 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urn:nbn:de:kobv:517-opus4-555943 urn 10.25932/publishup-55594 doi (DE-627)1813744173 (DE-599)KXP1813744173 (OCoLC)1339090366 DE-627 ger DE-627 rda eng XA-DE-BB 551.489 DE-101 550 DE-101 Brill, Fabio Alexander verfasserin (DE-588)1264926332 (DE-627)181376610X aut Applications of machine learning and open geospatial data in flood risk modelling by Fabio Alexander Brill Potsdam 2022 1 Online-Ressource (xix, 124 Seiten, 17891 KB) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation Volltext: PDF Literaturverzeichnis: Seite 97-124 Dissertation Universität Potsdam 2022 Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. [...] Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4025289-9 (DE-627)104141689 (DE-576)208959963 Hochwasser gnd s (DE-588)4257648-9 (DE-627)104671289 (DE-576)210565691 Hochwasserschaden gnd s (DE-588)4220751-4 (DE-627)105015490 (DE-576)210267461 Hochwasservorhersage gnd s (DE-588)4160306-0 (DE-627)10547875X (DE-576)209856742 Hochwasserwelle gnd s (DE-588)4047390-9 (DE-627)106194712 (DE-576)209073446 Prognose gnd s (DE-588)4202680-5 (DE-627)10515668X (DE-576)210150270 Katastrophenrisiko gnd (DE-627) s (DE-588)4293487-4 (DE-627)104193352 (DE-576)210952911 Überflutung gnd s (DE-588)4186634-4 (DE-627)104196092 (DE-576)210040432 Überschwemmung gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4453039-0 (DE-627)227482166 (DE-576)212592750 Überschwemmungsgefahr gnd s (DE-588)4170297-9 (DE-627)105403466 (DE-576)209929170 Modellierung gnd s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd (DE-627) s (DE-588)4142440-2 (DE-627)105613169 (DE-576)209718781 Angewandte Hydrologie gnd s (DE-588)4151469-5 (DE-627)105545570 (DE-576)209788429 Einzugsgebiet gnd s (DE-588)4443995-7 (DE-627)225305534 (DE-576)212501186 Überschwemmungsgebiet gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4029803-6 (DE-627)106272780 (DE-576)208984267 Kartierung gnd s (DE-588)7571300-7 (DE-627)529027003 (DE-576)265408997 Geoinformatik gnd (DE-627) s (DE-588)4020279-3 (DE-627)106318640 (DE-576)20893264X Geostatistik gnd s (DE-588)4802620-7 (DE-627)472310364 (DE-576)216543657 Big Data gnd s (DE-588)4428654-5 (DE-627)216935180 (DE-576)212347217 Data Mining gnd s (DE-588)4123037-1 (DE-627)105758051 (DE-576)209556331 Datenanalyse gnd s (DE-588)1135597375 (DE-627)890512922 (DE-576)489847412 Deep learning gnd s (DE-588)4040936-3 (DE-627)104360054 (DE-576)209042761 Mustererkennung gnd (DE-627) s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4056763-1 (DE-627)106153803 (DE-576)209118865 Stadtregion gnd s (DE-588)4131193-0 (DE-627)105696773 (DE-576)20962535X Datenauswertung gnd s (DE-588)1064023886 (DE-627)812870778 (DE-576)423564129 Open Data gnd s (DE-588)4206012-6 (DE-627)105131199 (DE-576)210172576 Raumdaten gnd s (DE-588)4453039-0 (DE-627)227482166 (DE-576)212592750 Überschwemmungsgefahr gnd (DE-627) Merz, Bruno akademischer betreuerin (DE-588)114734348 (DE-627)512977178 (DE-576)175880638 dgs Kreibich, Heidi 1969- akademischer betreuerin (DE-588)124201768 (DE-627)085703559 (DE-576)294067191 dgs Schumann, Guy akademischer betreuerin (DE-588)1171146396 (DE-627)1040388752 (DE-576)51360894X dgs Universität Potsdam Grad-verleihende Institution (DE-588)2120681-8 (DE-627)101653735 (DE-576)193975041 dgg Potsdam (DE-588)4046948-7 (DE-627)106196332 (DE-576)209071575 uvp Erscheint auch als Druck-Ausgabe Brill, Fabio Alexander Applications of machine learning and open geospatial data in flood risk modelling Potsdam, 2022 xix, 124 Seiten (DE-627)181374436X http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943 Resolving-System kostenfrei https://doi.org/10.25932/publishup-55594 2022-09-07 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-555943 2022-09-07 Resolving-System https://d-nb.info/1264210000/34 2022-09-07 Langzeitarchivierung Nationalbibliothek https://publishup.uni-potsdam.de/frontdoor/index/index/docId/55594 application/pdf 2022-09-07 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.489 20 01 0084 4185822146 x 08-09-22 21 01 0046 4185840446 z 08-09-22 22 01 0018 4185858361 SUBolrd xu 08-09-22 23 01 0830 4185876130 olr-d x 08-09-22 30 01 0104 4185889712 z 08-09-22 40 01 0007 4185901666 xsn 08-09-22 60 01 0705 4185919735 OLRD z 08-09-22 63 01 3401 4185936079 ORD x 08-09-22 70 01 0089 4185946120 zdo 08-09-22 105 01 0841 4186088225 z 08-09-22 110 01 3110 4185959451 x 08-09-22 132 01 0959 4185972423 OLR-DISS x 08-09-22 151 01 0546 4185987889 OLR-ODISS z 08-09-22 161 01 0960 4185995652 ORD z 08-09-22 285 01 0517 4176888504 00 9300 --%%-- s --%%-- Vervielfältigungen (z.B. 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Hochschulschrift |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a2200265 4500</leader><controlfield tag="001">1813744173</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220907222044.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 gw |||||om 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">urn:nbn:de:kobv:517-opus4-555943</subfield><subfield code="2">urn</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.25932/publishup-55594</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1813744173</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1813744173</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1339090366</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-DE-BB</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">551.489</subfield><subfield code="q">DE-101</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DE-101</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Brill, Fabio Alexander</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1264926332</subfield><subfield code="0">(DE-627)181376610X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Applications of machine learning and open geospatial data in flood risk modelling</subfield><subfield code="c">by Fabio Alexander Brill</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Potsdam</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xix, 124 Seiten, 17891 KB)</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Kumulative Dissertation</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Volltext: PDF</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Literaturverzeichnis: Seite 97-124</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="b">Dissertation</subfield><subfield code="c">Universität Potsdam</subfield><subfield code="d">2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. 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| author |
Brill, Fabio Alexander |
| spellingShingle |
Brill, Fabio Alexander ddc 551.489 ddc 550 gnd Hochwasser gnd Hochwasserschaden gnd Hochwasservorhersage gnd Hochwasserwelle gnd Prognose gnd Katastrophenrisiko gnd Überflutung gnd Überschwemmung gnd Risikoanalyse gnd Überschwemmungsgefahr gnd Modellierung gnd Maschinelles Lernen gnd Angewandte Hydrologie gnd Einzugsgebiet gnd Überschwemmungsgebiet gnd Fernerkundung gnd Kartierung gnd Geoinformatik gnd Geostatistik gnd Big Data gnd Data Mining gnd Datenanalyse gnd Deep learning gnd Mustererkennung gnd Wald gnd Stadtregion gnd Datenauswertung gnd Open Data gnd Raumdaten 2027 ebook Applications of machine learning and open geospatial data in flood risk modelling |
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Brill, Fabio Alexander |
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Applications of machine learning and open geospatial data in flood risk modelling |
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Brill, Fabio Alexander |
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1 Online-Ressource (xix, 124 Seiten, 17891 KB) Illustrationen, Diagramme |
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Applications of machine learning and open geospatial data in flood risk modelling |
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Brill, Fabio Brill, F. Brill, Fabio Alexander Hochschulschrift Flut <Hochwasser> Hochwasser Hochwasser / Schaden Hochwasser / Wasserschaden Hochwasserschaden Hochwasser / Prognose Hochwasserprognose Hochwasservorhersage Hochwasserwelle Zukunftsvoraussage Voraussage Vorhersage Prädiktion Zukunftsprognose Vorausschätzung Prognose Katastrophenrisiko Überfluten Vernässen Überflutung Überschwemmungen Überschwemmung Risikobewertung Risiko / Analyse Risk assessment Risikobeurteilung Risk analysis Risikoeinschätzung Risikoabschätzung Risikoanalyse Überschwemmung / Naturgefahr Hochwassergefahr Überschwemmungsgefahr Modellmethode Modellierungsmethode Modellbildung Modellierung Machine learning Automated learning Lernen <Künstliche Intelligenz> Algorithmisches Lernen Maschinelles Lernen Angewandte Hydrologie Einzugsbereich <Hydrologie> Einzugsgebiet Überschwemmung / Gebiet Überschwemmungsgebiet Remote sensing Fernerkundung Kartierung Geomatik Spatial Informatics Geowissenschaften / Angewandte Informatik Geomatics Geoinformatics Geo-Informatics Geoinformatik Geowissenschaften / Statistik Geostatistik Massendaten Mass Data Big Data Datamining Data-Mining Datenmustererkennung Data Mining Data-analysis Statistische Auswertung Statistische Datenanalyse Data analysis Datenauswertung <Statistik> Datenanalyse Tiefgehendes Lernen Deep learning Strukturerkennung Strukturwahrnehmung Pattern Perception Pattern Recognition Musterwahrnehmung Mustererkennung Wälder Wald Stadtregion Datenauswertung Offene Daten Open Data Bewegung Open Data Geografie / Datensammlung Raumbezogene Daten Raumordnung / Datensammlung Geodaten Raumdaten Merz, Bruno Kreibich, Heidi Schumann, Guy J-P. Schumann, G. Schumann, Guy Uni Potsdam Potsdamer Universität Univ. Potsdam University of Potsdam Universität Potsdam Bostanium Postampium Poztupimi Potzdam Bostampium Potsdamum State Capital of Potsdam Stadtgemeinde Potsdam Residenzstadt Potsdam Potestampium Potsdam-Sanssouci Pozdam Potsdamm Landeshauptstadt Potsdam Potsdam |
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Brill, Fabio Brill, F. Brill, Fabio Alexander Hochschulschrift Flut <Hochwasser> Hochwasser Hochwasser / Schaden Hochwasser / Wasserschaden Hochwasserschaden Hochwasser / Prognose Hochwasserprognose Hochwasservorhersage Hochwasserwelle Zukunftsvoraussage Voraussage Vorhersage Prädiktion Zukunftsprognose Vorausschätzung Prognose Katastrophenrisiko Überfluten Vernässen Überflutung Überschwemmungen Überschwemmung Risikobewertung Risiko / Analyse Risk assessment Risikobeurteilung Risk analysis Risikoeinschätzung Risikoabschätzung Risikoanalyse Überschwemmung / Naturgefahr Hochwassergefahr Überschwemmungsgefahr Modellmethode Modellierungsmethode Modellbildung Modellierung Machine learning Automated learning Lernen <Künstliche Intelligenz> Algorithmisches Lernen Maschinelles Lernen Angewandte Hydrologie Einzugsbereich <Hydrologie> Einzugsgebiet Überschwemmung / Gebiet Überschwemmungsgebiet Remote sensing Fernerkundung Kartierung Geomatik Spatial Informatics Geowissenschaften / Angewandte Informatik Geomatics Geoinformatics Geo-Informatics Geoinformatik Geowissenschaften / Statistik Geostatistik Massendaten Mass Data Big Data Datamining Data-Mining Datenmustererkennung Data Mining Data-analysis Statistische Auswertung Statistische Datenanalyse Data analysis Datenauswertung <Statistik> Datenanalyse Tiefgehendes Lernen Deep learning Strukturerkennung Strukturwahrnehmung Pattern Perception Pattern Recognition Musterwahrnehmung Mustererkennung Wälder Wald Stadtregion Datenauswertung Offene Daten Open Data Bewegung Open Data Geografie / Datensammlung Raumbezogene Daten Raumordnung / Datensammlung Geodaten Raumdaten Merz, Bruno Kreibich, Heidi Schumann, Guy J-P. Schumann, G. Schumann, Guy Uni Potsdam Potsdamer Universität Univ. Potsdam University of Potsdam Universität Potsdam Bostanium Postampium Poztupimi Potzdam Bostampium Potsdamum State Capital of Potsdam Stadtgemeinde Potsdam Residenzstadt Potsdam Potestampium Potsdam-Sanssouci Pozdam Potsdamm Landeshauptstadt Potsdam Potsdam |
| GND_txtF_mv |
Brill, Fabio Brill, F. Brill, Fabio Alexander Hochschulschrift Flut <Hochwasser> Hochwasser Hochwasser / Schaden Hochwasser / Wasserschaden Hochwasserschaden Hochwasser / Prognose Hochwasserprognose Hochwasservorhersage Hochwasserwelle Zukunftsvoraussage Voraussage Vorhersage Prädiktion Zukunftsprognose Vorausschätzung Prognose Katastrophenrisiko Überfluten Vernässen Überflutung Überschwemmungen Überschwemmung Risikobewertung Risiko / Analyse Risk assessment Risikobeurteilung Risk analysis Risikoeinschätzung Risikoabschätzung Risikoanalyse Überschwemmung / Naturgefahr Hochwassergefahr Überschwemmungsgefahr Modellmethode Modellierungsmethode Modellbildung Modellierung Machine learning Automated learning Lernen <Künstliche Intelligenz> Algorithmisches Lernen Maschinelles Lernen Angewandte Hydrologie Einzugsbereich <Hydrologie> Einzugsgebiet Überschwemmung / Gebiet Überschwemmungsgebiet Remote sensing Fernerkundung Kartierung Geomatik Spatial Informatics Geowissenschaften / Angewandte Informatik Geomatics Geoinformatics Geo-Informatics Geoinformatik Geowissenschaften / Statistik Geostatistik Massendaten Mass Data Big Data Datamining Data-Mining Datenmustererkennung Data Mining Data-analysis Statistische Auswertung Statistische Datenanalyse Data analysis Datenauswertung <Statistik> Datenanalyse Tiefgehendes Lernen Deep learning Strukturerkennung Strukturwahrnehmung Pattern Perception Pattern Recognition Musterwahrnehmung Mustererkennung Wälder Wald Stadtregion Datenauswertung Offene Daten Open Data Bewegung Open Data Geografie / Datensammlung Raumbezogene Daten Raumordnung / Datensammlung Geodaten Raumdaten Merz, Bruno Kreibich, Heidi Schumann, Guy J-P. Schumann, G. Schumann, Guy Uni Potsdam Potsdamer Universität Univ. Potsdam University of Potsdam Universität Potsdam Bostanium Postampium Poztupimi Potzdam Bostampium Potsdamum State Capital of Potsdam Stadtgemeinde Potsdam Residenzstadt Potsdam Potestampium Potsdam-Sanssouci Pozdam Potsdamm Landeshauptstadt Potsdam Potsdam |
| doi_str |
10.25932/publishup-55594 |
| callnumber-a |
--%%-- |
| up_date |
2024-07-04T13:57:19.020Z |
| _version_ |
1803657086528126976 |
| fullrecord_marcxml |
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For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail. A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. 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