Predicting Water Permeability of the Soil Based on Open Data - Artificial Intelligence Applications and Innovations (AIAI 2014) Access content directly
Conference Papers Year : 2014

Predicting Water Permeability of the Soil Based on Open Data

Jonne Pohjankukka
  • Function : Author
  • PersonId : 992483
Paavo Nevalainen
  • Function : Author
  • PersonId : 992484
Tapio Pahikkala
  • Function : Author
  • PersonId : 992485
Eija Hyvönen
  • Function : Author
  • PersonId : 992486
Pekka Hänninen
  • Function : Author
  • PersonId : 992487
Raimo Sutinen
  • Function : Author
  • PersonId : 992488
Jari Ala-Ilomäki
  • Function : Author
  • PersonId : 992489
Jukka Heikkonen
  • Function : Author
  • PersonId : 992490

Abstract

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.
Fichier principal
Vignette du fichier
978-3-662-44654-6_43_Chapter.pdf (1.31 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01391345 , version 1 (03-11-2016)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Jonne Pohjankukka, Paavo Nevalainen, Tapio Pahikkala, Eija Hyvönen, Pekka Hänninen, et al.. Predicting Water Permeability of the Soil Based on Open Data. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.436-446, ⟨10.1007/978-3-662-44654-6_43⟩. ⟨hal-01391345⟩
50 View
107 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More