Predicting Water Permeability of the Soil Based on Open Data
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...
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ftccsdartic:oai:HAL:hal-01391345v1 2023-05-15T14:49:38+02:00 Predicting Water Permeability of the Soil Based on Open Data Pohjankukka, Jonne Nevalainen, Paavo Pahikkala, Tapio Hyvönen, Eija Hänninen, Pekka Sutinen, Raimo Ala-Ilomäki, Jari Heikkonen, Jukka University of Turku Lazaros Iliadis Ilias Maglogiannis Harris Papadopoulos TC 12 WG 12.5 Rhodes, Greece 2014-09-19 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 en eng HAL CCSD Springer info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-662-44654-6_43 hal-01391345 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 doi:10.1007/978-3-662-44654-6_43 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess IFIP Advances in Information and Communication Technology 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) https://hal.inria.fr/hal-01391345 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⟩ load bearing capacity of soil water permeability regression k-nearest neighbor mobility sub-arctic infrastructure [INFO]Computer Science [cs] info:eu-repo/semantics/conferenceObject Conference papers 2014 ftccsdartic https://doi.org/10.1007/978-3-662-44654-6_43 2020-12-25T14:11:02Z 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. Conference Object Arctic Northern Finland Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Arctic 436 446 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
load bearing capacity of soil water permeability regression k-nearest neighbor mobility sub-arctic infrastructure [INFO]Computer Science [cs] |
spellingShingle |
load bearing capacity of soil water permeability regression k-nearest neighbor mobility sub-arctic infrastructure [INFO]Computer Science [cs] Pohjankukka, Jonne Nevalainen, Paavo Pahikkala, Tapio Hyvönen, Eija Hänninen, Pekka Sutinen, Raimo Ala-Ilomäki, Jari Heikkonen, Jukka Predicting Water Permeability of the Soil Based on Open Data |
topic_facet |
load bearing capacity of soil water permeability regression k-nearest neighbor mobility sub-arctic infrastructure [INFO]Computer Science [cs] |
description |
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. |
author2 |
University of Turku Lazaros Iliadis Ilias Maglogiannis Harris Papadopoulos TC 12 WG 12.5 |
format |
Conference Object |
author |
Pohjankukka, Jonne Nevalainen, Paavo Pahikkala, Tapio Hyvönen, Eija Hänninen, Pekka Sutinen, Raimo Ala-Ilomäki, Jari Heikkonen, Jukka |
author_facet |
Pohjankukka, Jonne Nevalainen, Paavo Pahikkala, Tapio Hyvönen, Eija Hänninen, Pekka Sutinen, Raimo Ala-Ilomäki, Jari Heikkonen, Jukka |
author_sort |
Pohjankukka, Jonne |
title |
Predicting Water Permeability of the Soil Based on Open Data |
title_short |
Predicting Water Permeability of the Soil Based on Open Data |
title_full |
Predicting Water Permeability of the Soil Based on Open Data |
title_fullStr |
Predicting Water Permeability of the Soil Based on Open Data |
title_full_unstemmed |
Predicting Water Permeability of the Soil Based on Open Data |
title_sort |
predicting water permeability of the soil based on open data |
publisher |
HAL CCSD |
publishDate |
2014 |
url |
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 |
op_coverage |
Rhodes, Greece |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Northern Finland |
genre_facet |
Arctic Northern Finland |
op_source |
IFIP Advances in Information and Communication Technology 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) https://hal.inria.fr/hal-01391345 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⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-662-44654-6_43 hal-01391345 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 doi:10.1007/978-3-662-44654-6_43 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1007/978-3-662-44654-6_43 |
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436 |
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446 |
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1766320710086557696 |