{"id":{"repo_id":"aalto","oai_identifier":"oai:aaltodoc.aalto.fi:123456789/2864"},"canonical_url":"https://search.dev.ndltd.org/etd/aalto/oai:aaltodoc.aalto.fi:123456789/2864","repository":{"repo_id":"aalto","name":"Aalto University","base_url":"https://aaltodoc.aalto.fi/server/oai/request"},"display":{"title":"Water quality prediction for river basin management","abstract":"Water quality prediction methods are developed which provide realistic estimates of prediction errors and accordingly increase the efficiency of river basin management and the implementation of EU's Water Framework Directive. The resulting river basin management decisions are based on realistic safety margins for restoration measures and accompanying targeted pollutant load limits. The realistic error estimates attached to the predictions are based on Bayesian statistical inference and MCMC methods which are able to synthesize two distinct water quality prediction approaches i.e. mechanistic and statistical. What is more, a hierarchical modeling strategy is employed in order to pool information from extensive cross-sectional lake monitoring data and consequently to improve the accuracy and precision of lake specific water quality predictions. Testing of the methods using extensive hydrological and water quality data from five real-world river basin management cases suggests that Bayesian inference and MCMC methods are no more difficult to implement than classical statistical methods. Even models with large numbers of correlated parameters can be fitted using modern computational methods. Moreover, the hierarchical modeling strategy proves to be efficient for river basin management. Guidelines for adaptive river basin management are also set up based on the experience gained. It is proposed that monitoring, prediction and decision making should be integrated into an efficient management procedure.","abstract_html":"Water quality prediction methods are developed which provide realistic estimates of prediction errors and accordingly increase the efficiency of river basin management and the implementation of EU's Water Framework Directive. The resulting river basin management decisions are based on realistic safety margins for restoration measures and accompanying targeted pollutant load limits. The realistic error estimates attached to the predictions are based on Bayesian statistical inference and MCMC methods which are able to synthesize two distinct water quality prediction approaches i.e. mechanistic and statistical. What is more, a hierarchical modeling strategy is employed in order to pool information from extensive cross-sectional lake monitoring data and consequently to improve the accuracy and precision of lake specific water quality predictions. Testing of the methods using extensive hydrological and water quality data from five real-world river basin management cases suggests that Bayesian inference and MCMC methods are no more difficult to implement than classical statistical methods. Even models with large numbers of correlated parameters can be fitted using modern computational methods. Moreover, the hierarchical modeling strategy proves to be efficient for river basin management. Guidelines for adaptive river basin management are also set up based on the experience gained. It is proposed that monitoring, prediction and decision making should be integrated into an efficient management procedure.","abstract_has_math":false,"creators":["Malve, Olli"],"institution":"Helsinki University of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Department of Civil and Environmental Engineering","school":null,"contributors":["Aalto-yliopisto","Aalto University"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-05-18","date_published":"2007-05-18","updated_at":"2026-08-21T16:42:07Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aaltodoc.aalto.fi/handle/123456789/2864","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://aaltodoc.aalto.fi/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aaaltodoc.aalto.fi%3A123456789%2F2864","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aalto-yliopisto","Aalto University"]},{"key":"dc:contributor.department","label":"Department","values":["Department of Civil and Environmental Engineering","Rakennus- ja ympäristötekniikan osasto"]},{"key":"dc:creator","label":"Author","values":["Malve, Olli"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-02-24T08:17:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-02-24T08:17:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2007-05-18"]},{"key":"dc:publisher","label":"Institution","values":["Helsinki University of Technology","Teknillinen korkeakoulu"]},{"key":"dc:type","label":"Dc Type","values":["G5 Artikkeliväitöskirja"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["text"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://aaltodoc.aalto.fi/handle/123456789/2864"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Water quality prediction methods are developed which provide realistic estimates of prediction errors and accordingly increase the efficiency of river basin management and the implementation of EU's Water Framework Directive. The resulting river basin management decisions are based on realistic safety margins for restoration measures and accompanying targeted pollutant load limits. The realistic error estimates attached to the predictions are based on Bayesian statistical inference and MCMC methods which are able to synthesize two distinct water quality prediction approaches i.e. mechanistic and statistical. What is more, a hierarchical modeling strategy is employed in order to pool information from extensive cross-sectional lake monitoring data and consequently to improve the accuracy and precision of lake specific water quality predictions. Testing of the methods using extensive hydrological and water quality data from five real-world river basin management cases suggests that Bayesian inference and MCMC methods are no more difficult to implement than classical statistical methods. Even models with large numbers of correlated parameters can be fitted using modern computational methods. Moreover, the hierarchical modeling strategy proves to be efficient for river basin management. Guidelines for adaptive river basin management are also set up based on the experience gained. It is proposed that monitoring, prediction and decision making should be integrated into an efficient management procedure.","Tässä työssä kehitetään ja testataan vedenlaadun ennustemenetelmiä, jotka antavat realistisen kuvan ennustevirheistä, auttavat välttämään vesistöalueiden hoitotoimien virhemitoituksen sekä tehostavat EU'n vesipuitedirektiivin toimeenpanoa vastaavasti. Laskenta perustuu Bayeslaisen päättelyyn ja MCMC-menetelmään, jotka mahdollistavat mekanistisen vedenlaatumallin ennustevirheen realistisen estimoinnin. Lisäksi sovelletaan hierarkista mallintamisstrategiaa järvikohtaiseen vedenlaadun ennustamiseen laajan suomalaisen järviseuranta-aineiston perusteella. Valittu strategia pienentää ennustevirheitä ja parantaa ennusteiden tarkkuutta. Menetelmiä testataan Lappajärven, Kymijoen, Tuusulanjärven, Säkylän Pyhäjärven sekä yli kahden tuhannen, Suomen ympäristökeskuksen seurantaverkossa olevan järven hoitotoimien tavoitteen asettelussa. Testit osoittavat, että Bayes-päättelyn ja MCMC-menetelmän ja klassisten tilastomatemaattisten menetelmien laskennallinen toteutus on yhtä helppoa. Jopa suuren määrän korreloituneita parametrejä sisältävä vedenlaatumalli saadaan sovitettua havaintoaineistoon. Myös hierarkinen mallintamisstrategia on tehokas väline vesistökohtaisten vedenlaatuennusteiden tekemisessä ja vesistöalueiden hoidon suunnittelussa. Lopussa annetaan suosituksia vesistöseurannan, vedenlaadun ennustamisen sekä vesistöjen hoidon yhdistämisestä tässä työssä kehitettyjen laskentamenetelmien avulla jatkuvasti tarkentuvaksi, adaptiiviseksi hoitoprosessiksi."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Water quality prediction for river basin management","Vedenlaadun ennustaminen vesistöaluiden hoidon suunnittelussa"]}]}],"canonical_facts":{"dc:contributor":["Aalto-yliopisto","Aalto University"],"dc:contributor.department":["Department of Civil and Environmental Engineering","Rakennus- ja ympäristötekniikan osasto"],"dc:creator":["Malve, Olli"],"dc:date.accessioned":["2012-02-24T08:17:58Z"],"dc:date.available":["2012-02-24T08:17:58Z"],"dc:date.issued":["2007-05-18"],"dc:description.abstract":["Water quality prediction methods are developed which provide realistic estimates of prediction errors and accordingly increase the efficiency of river basin management and the implementation of EU's Water Framework Directive. The resulting river basin management decisions are based on realistic safety margins for restoration measures and accompanying targeted pollutant load limits. The realistic error estimates attached to the predictions are based on Bayesian statistical inference and MCMC methods which are able to synthesize two distinct water quality prediction approaches i.e. mechanistic and statistical. What is more, a hierarchical modeling strategy is employed in order to pool information from extensive cross-sectional lake monitoring data and consequently to improve the accuracy and precision of lake specific water quality predictions. Testing of the methods using extensive hydrological and water quality data from five real-world river basin management cases suggests that Bayesian inference and MCMC methods are no more difficult to implement than classical statistical methods. Even models with large numbers of correlated parameters can be fitted using modern computational methods. Moreover, the hierarchical modeling strategy proves to be efficient for river basin management. Guidelines for adaptive river basin management are also set up based on the experience gained. It is proposed that monitoring, prediction and decision making should be integrated into an efficient management procedure.","Tässä työssä kehitetään ja testataan vedenlaadun ennustemenetelmiä, jotka antavat realistisen kuvan ennustevirheistä, auttavat välttämään vesistöalueiden hoitotoimien virhemitoituksen sekä tehostavat EU'n vesipuitedirektiivin toimeenpanoa vastaavasti. Laskenta perustuu Bayeslaisen päättelyyn ja MCMC-menetelmään, jotka mahdollistavat mekanistisen vedenlaatumallin ennustevirheen realistisen estimoinnin. Lisäksi sovelletaan hierarkista mallintamisstrategiaa järvikohtaiseen vedenlaadun ennustamiseen laajan suomalaisen järviseuranta-aineiston perusteella. Valittu strategia pienentää ennustevirheitä ja parantaa ennusteiden tarkkuutta. Menetelmiä testataan Lappajärven, Kymijoen, Tuusulanjärven, Säkylän Pyhäjärven sekä yli kahden tuhannen, Suomen ympäristökeskuksen seurantaverkossa olevan järven hoitotoimien tavoitteen asettelussa. Testit osoittavat, että Bayes-päättelyn ja MCMC-menetelmän ja klassisten tilastomatemaattisten menetelmien laskennallinen toteutus on yhtä helppoa. Jopa suuren määrän korreloituneita parametrejä sisältävä vedenlaatumalli saadaan sovitettua havaintoaineistoon. Myös hierarkinen mallintamisstrategia on tehokas väline vesistökohtaisten vedenlaatuennusteiden tekemisessä ja vesistöalueiden hoidon suunnittelussa. Lopussa annetaan suosituksia vesistöseurannan, vedenlaadun ennustamisen sekä vesistöjen hoidon yhdistämisestä tässä työssä kehitettyjen laskentamenetelmien avulla jatkuvasti tarkentuvaksi, adaptiiviseksi hoitoprosessiksi."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://aaltodoc.aalto.fi/handle/123456789/2864"],"dc:language.iso":["en"],"dc:publisher":["Helsinki University of Technology","Teknillinen korkeakoulu"],"dc:title":["Water quality prediction for river basin management","Vedenlaadun ennustaminen vesistöaluiden hoidon suunnittelussa"],"dc:type":["G5 Artikkeliväitöskirja"],"dc:type.dcmitype":["text"]},"updated_at":"2026-08-21T16:42:07Z"}