Predicting Water Permeability of the Soil Based on Open Data

• Main Authors: Pohjankukka, Jonne, Nevalainen, Paavo, Pahikkala, Tapio, Hyvönen, Eija, Hänninen, Pekka, Sutinen, Raimo, Ala-Ilomäki, Jari, Heikkonen, Jukka
• Other Authors: University of Turku, Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, TC 12, WG 12.5
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: load bearing capacity of soil; water permeability; regression; k-nearest neighbor; mobility; sub-arctic infrastructure; [INFO]Computer Science [cs]; Arctic; Northern Finland
• Online Access: https://hal.inria.fr/hal-01391345; https://hal.inria.fr/hal-01391345/document; https://hal.inria.fr/hal-01391345/file/978-3-662-44654-6_43_Chapter.pdf; https://doi.org/10.1007/978-3-662-44654-6_43

Part 10: Environmental AI International audience 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.