{"id":{"repo_id":"humboldt-diss","oai_identifier":"oai:edoc.hu-berlin.de:18452/14812"},"canonical_url":"https://search.dev.ndltd.org/etd/humboldt-diss/oai:edoc.hu-berlin.de:18452/14812","repository":{"repo_id":"humboldt-diss","name":"Humboldt Universität zu Berlin","base_url":"https://edoc.hu-berlin.de/server/oai/request"},"display":{"title":"Water Level Modeling around German Bight","abstract":"This work investigates the water level data measured in 18 stations around the German Bight from 1953 to 2006. It is the most useful hydrometric data to measure a water body and to do a forecasting for specific extreme risks. Our water level data are both temporal and spatial. We apply first stochastic time series models to the data on temporal level. The model has four patterns: trend, seasonality, autoregressive components and the heteroscedastic residuals captured by a dynamics conditional volatility model. Two different procedures are applied in this work to model the conditional mean dynamics. After the comparison of the empirical results from all procedures, we get the residuals from the \"best\" approach. Afterwards we adopt the spatial analysis to the residuals on each day, in order to interpolate for the unobserved locations. Different variogram and Kriging models are applied and compared.","abstract_html":"This work investigates the water level data measured in 18 stations around the German Bight from 1953 to 2006. It is the most useful hydrometric data to measure a water body and to do a forecasting for specific extreme risks. Our water level data are both temporal and spatial. We apply first stochastic time series models to the data on temporal level. The model has four patterns: trend, seasonality, autoregressive components and the heteroscedastic residuals captured by a dynamics conditional volatility model. Two different procedures are applied in this work to model the conditional mean dynamics. After the comparison of the empirical results from all procedures, we get the residuals from the "best" approach. Afterwards we adopt the spatial analysis to the residuals on each day, in order to interpolate for the unobserved locations. Different variogram and Kriging models are applied and compared.","abstract_has_math":false,"creators":["Cao, Xiaofeng"],"institution":"Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-02-15","date_published":"2012-02-15","updated_at":"2026-08-21T16:45:14Z","subjects":["seasonality","water level analysis","variogram","Kriging"],"languages":["eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.18452/14160"],"render_values":[{"text":"https://doi.org/10.18452/14160","href":"https://doi.org/10.18452/14160","code":true}]}]},"links":{"outbound_url":"https://edoc.hu-berlin.de/18452/14812","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://edoc.hu-berlin.de/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aedoc.hu-berlin.de%3A18452%2F14812","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Cao, Xiaofeng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-06-18T02:31:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-06-18T02:31:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2012-02-15"]},{"key":"dc:publisher","label":"Institution","values":["Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät"]},{"key":"dc:type","label":"Dc Type","values":["masterThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["seasonality","water level analysis","variogram","Kriging"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.18452/14160"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://edoc.hu-berlin.de/18452/14812"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This work investigates the water level data measured in 18 stations around the German Bight from 1953 to 2006. It is the most useful hydrometric data to measure a water body and to do a forecasting for specific extreme risks. Our water level data are both temporal and spatial. We apply first stochastic time series models to the data on temporal level. The model has four patterns: trend, seasonality, autoregressive components and the heteroscedastic residuals captured by a dynamics conditional volatility model. Two different procedures are applied in this work to model the conditional mean dynamics. After the comparison of the empirical results from all procedures, we get the residuals from the \"best\" approach. Afterwards we adopt the spatial analysis to the residuals on each day, in order to interpolate for the unobserved locations. Different variogram and Kriging models are applied and compared."]},{"key":"dc:title","label":"Title","values":["Water Level Modeling around German Bight"]}]}],"canonical_facts":{"dc:creator":["Cao, Xiaofeng"],"dc:date.accessioned":["2017-06-18T02:31:39Z"],"dc:date.available":["2017-06-18T02:31:39Z"],"dc:date.issued":["2012-02-15"],"dc:description.abstract":["This work investigates the water level data measured in 18 stations around the German Bight from 1953 to 2006. It is the most useful hydrometric data to measure a water body and to do a forecasting for specific extreme risks. Our water level data are both temporal and spatial. We apply first stochastic time series models to the data on temporal level. The model has four patterns: trend, seasonality, autoregressive components and the heteroscedastic residuals captured by a dynamics conditional volatility model. Two different procedures are applied in this work to model the conditional mean dynamics. After the comparison of the empirical results from all procedures, we get the residuals from the \"best\" approach. Afterwards we adopt the spatial analysis to the residuals on each day, in order to interpolate for the unobserved locations. Different variogram and Kriging models are applied and compared."],"dc:identifier.doi":["https://doi.org/10.18452/14160"],"dc:identifier.uri":["https://edoc.hu-berlin.de/18452/14812"],"dc:language.iso":["eng"],"dc:publisher":["Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["seasonality","water level analysis","variogram","Kriging"],"dc:title":["Water Level Modeling around German Bight"],"dc:type":["masterThesis"]},"updated_at":"2026-08-21T16:45:14Z"}