A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Predicting water permeability of the soil based on open data
Tekijät: Jonne Pohjankukka, Paavo Nevalainen, Tapio Pahikkala, Eija Hyvönen, Pekka Hänninen, Raimo Sutinen, Jari Ala-Ilomäki, Jukka Heikkonen
Toimittaja: Iliadis Lazaros, Maglogiannis Ilias, Papadopoulos Harris
Konferenssin vakiintunut nimi: International Conference on Artificial Intelligence Applications and Innovations
Julkaisuvuosi: 2014
Journal: IFIP Advances in Information and Communication Technology
Kokoomateoksen nimi: Artificial Intelligence Applications and Innovations: 10th IFIP WG 12.5 International Conference, AIAI 2014, Rhodes, Greece, September 19-21, 2014. Proceedings
Sarjan nimi: IFIP Advances in Information and Communication Technology
Numero sarjassa: 436
Vuosikerta: 436
Aloitussivu: 436
Lopetussivu: 446
ISBN: 978-3-662-44654-6
ISSN: 1868-4238
DOI: https://doi.org/10.1007/978-3-662-44654-6_43
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/1789834
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.
Ladattava julkaisu This is an electronic reprint of the original article. |