Predicting water permeability of the soil based on open data

dc.contributor.authorJonne Pohjankukka
dc.contributor.authorPaavo Nevalainen
dc.contributor.authorTapio Pahikkala
dc.contributor.authorEija Hyvönen
dc.contributor.authorPekka Hänninen
dc.contributor.authorRaimo Sutinen
dc.contributor.authorJari Ala-Ilomäki
dc.contributor.authorJukka Heikkonen
dc.contributor.organizationfi=tietojenkäsittelytiede|en=Computer Science|
dc.contributor.organizationfi=tietotekniikan laitos|en=Department of Computing|
dc.contributor.organization-code1.2.246.10.2458963.20.23479734818
dc.contributor.organization-code1.2.246.10.2458963.20.85312822902
dc.converis.publication-id1789834
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/1789834
dc.date.accessioned2022-10-28T13:36:31Z
dc.date.available2022-10-28T13:36:31Z
dc.description.abstract<p> Water permeability is a key concept when estimating load bearing capacity, mobility and infrastructure potential of a terrain. Northern sub-arctic areas have rather similar dominant soil types and thus prediction methods successful at Northern Finland may generalize to other arctic areas. In this paper we have predicted water permeability using publicly available natural resource data with regression analysis. The data categories used for regression were: airborne electro-magnetic and radiation, topographic height, national forest inventory data, and peat bog thickness. Various additional features were derived from original data to enable better predictions. The regression performances indicate that the prediction capability exists up to 120 meters from the closest direct measurement points. The results were measured using leave-one-out cross-validation with a dead zone between the training and testing data sets.</p>
dc.format.pagerange436
dc.format.pagerange446
dc.identifier.isbn978-3-662-44654-6
dc.identifier.issn1868-4238
dc.identifier.jour-issn1868-4238
dc.identifier.olddbid183074
dc.identifier.oldhandle10024/166168
dc.identifier.urihttps://www.utupub.fi/handle/11111/40426
dc.identifier.urnURN:NBN:fi-fe2021042714292
dc.okm.affiliatedauthorPohjankukka, Jonne
dc.okm.affiliatedauthorNevalainen, Paavo
dc.okm.affiliatedauthorHeikkonen, Jukka
dc.okm.affiliatedauthorPahikkala, Tapio
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA4 Conference Article
dc.relation.conferenceInternational Conference on Artificial Intelligence Applications and Innovations
dc.relation.doi10.1007/978-3-662-44654-6_43
dc.relation.ispartofjournalIFIP Advances in Information and Communication Technology
dc.relation.ispartofseriesIFIP Advances in Information and Communication Technology
dc.relation.volume436
dc.source.identifierhttps://www.utupub.fi/handle/10024/166168
dc.titlePredicting water permeability of the soil based on open data
dc.title.bookArtificial Intelligence Applications and Innovations: 10th IFIP WG 12.5 International Conference, AIAI 2014, Rhodes, Greece, September 19-21, 2014. Proceedings
dc.year.issued2014

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