A4 Vertaisarvioitu artikkeli konferenssijulkaisussa

Predicting water permeability of the soil based on open data




TekijätJonne Pohjankukka, Paavo Nevalainen, Tapio Pahikkala, Eija Hyvönen, Pekka Hänninen, Raimo Sutinen, Jari Ala-Ilomäki, Jukka Heikkonen

ToimittajaIliadis Lazaros, Maglogiannis Ilias, Papadopoulos Harris

Konferenssin vakiintunut nimiInternational Conference on Artificial Intelligence Applications and Innovations

Julkaisuvuosi2014

JournalIFIP Advances in Information and Communication Technology

Kokoomateoksen nimiArtificial Intelligence Applications and Innovations: 10th IFIP WG 12.5 International Conference, AIAI 2014, Rhodes, Greece, September 19-21, 2014. Proceedings

Sarjan nimiIFIP Advances in Information and Communication Technology

Numero sarjassa436

Vuosikerta436

Aloitussivu436

Lopetussivu446

ISBN978-3-662-44654-6

ISSN1868-4238

DOIhttps://doi.org/10.1007/978-3-662-44654-6_43

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/1789834


Tiivistelmä

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.


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