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2020-02-25Zeitschriftenartikel DOI: 10.3390/w12030617
Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones
dc.contributor.authorGericke, Andreas
dc.contributor.authorNguyen, Hong Hanh
dc.contributor.authorFischer, Peter
dc.contributor.authorKail, Jochem
dc.contributor.authorVenohr, Markus
dc.date.accessioned2020-10-01T07:18:16Z
dc.date.available2020-10-01T07:18:16Z
dc.date.issued2020-02-25none
dc.date.updated2020-03-05T22:35:36Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/22668
dc.description.abstractBayesian networks (BN) have increasingly been applied in water management but not to estimate the efficacy of riparian buffer zones (RBZ). Our methodical study aims at evaluating the first BN to predict the RBZ efficacy to retain sediment and nutrients (dissolved, total, and particulate nitrogen and phosphorus) from widely available variables (width, vegetation, slope, soil texture, flow pathway, nutrient form). To evaluate the influence of parent nodes and how the number of states affects prediction errors, we used a predefined general BN structure, collected 580 published datasets from North America and Europe, and performed classification tree analyses and multiple 10-fold cross-validations of different BNs. These errors ranged from 0.31 (two output states) to 0.66 (five states). The outcome remained unchanged without the least influential nodes (flow pathway, vegetation). Lower errors were achieved when parent nodes had more than two states. The number of efficacy states influenced most strongly the prediction error as its lowest and highest states were better predicted than intermediate states. While the derived BNs could support or replace simple design guidelines, they are limited for more detailed predictions. More representative data on vegetation or additional nodes like preferential flow will probably improve the predictive power.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectmodel evaluationeng
dc.subjectnitrogeneng
dc.subjectnutrient retentioneng
dc.subjectphosphoruseng
dc.subjectsedimenteng
dc.subject.ddc690 Bau von Gebäudennone
dc.titleDeriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zonesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/22668-6
dc.identifier.doi10.3390/w12030617none
dc.identifier.doihttp://dx.doi.org/10.18452/21991
dc.type.versionpublishedVersionnone
local.edoc.container-titleWaternone
local.edoc.pages21none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameMDPInone
local.edoc.container-publisher-placeBaselnone
local.edoc.container-volume12none
local.edoc.container-issue3none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber617none
dc.identifier.eissn2073-4441
local.edoc.affiliationGericke, Andreas; Leibniz-Institute of Freshwater Ecology and Inland Fisheries, 12489 Berlin, Germany, gericke@igb-berlin.denone
local.edoc.affiliationNguyen, Hong Hanh; Leibniz-Institute of Freshwater Ecology and Inland Fisheries, 12489 Berlin, Germany, hanh.nguyen@igb-berlin.denone
local.edoc.affiliationFischer, Peter; Leibniz-Institute of Freshwater Ecology and Inland Fisheries, 12489 Berlin, Germany, peter_fischer@online.de Center for Agricultural Technology Augustenberg, 76227 Karlsruhe, Germany, peter_fischer@online.denone
local.edoc.affiliationKail, Jochem; Faculty of Biology, Department of Aquatic Ecology, University of Duisburg-Essen, 45141 Essen, Germany, jochem.kail@uni-due.denone
local.edoc.affiliationVenohr, Markus; Leibniz-Institute of Freshwater Ecology and Inland Fisheries, 12489 Berlin, Germany, m.venohr@igb-berlin.de Department of Geography, Humboldt-University of Berlin, 12489 Berlin, Germany, m.venohr@igb-berlin.denone

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