Abstract
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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