University of Oulu

Nasim, S., Oussalah, M., Klöve, B. et al. Machine learning model for snow depth estimation using a multisensory ubiquitous platform. J. Mt. Sci. 19, 2506–2527 (2022).

Machine learning model for snow depth estimation using a multisensory ubiquitous platform

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Author: Nasim, Sofeem1; Oussalah, Mourad1; Klöve, Björn2;
Organizations: 1Department of Computer Science and Engineering, University of Oulu, Oulu, 90570, Finland
2Water, Energy and Environmental Engineering, University of Oulu, Oulu, 90570, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.5 MB)
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Language: English
Published: Springer Nature, 2022
Publish Date: 2023-05-31


Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction. Despite advances in remote sensing technology and enhanced satellite observations, the estimation of snow depth at local scale still requires improved accuracy and flexibility. The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge. In this paper, a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward. Significantly, the results of Random Forest classifier showed an accuracy of 94%, indicating a promising alternative in snow depth measurement compared to in situ, LiDAR, or expensive large-scale wireless sensor network, which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.

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Series: Journal of mountain science
ISSN: 1672-6316
ISSN-E: 1993-0321
ISSN-L: 1672-6316
Volume: 19
Issue: 9
Pages: 2506 - 2527
DOI: 10.1007/s11629-021-7186-4
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Funding: This work is partly supported by the European Regional Funding Project IPaWa (2019-2022) Innovative Urban Planning and Storm water Management in a Resilient and Smart Cities.
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