Development of a Model to Estimate Parameters in a Snowpack Based on Capacitive Measurements

EasyChair Preprint 4140

Abstract

In the optimization process of hydropower production, it is relevant to consider some information about the snowpack’s parameters. Today, several techniques and devices to measure density, height, and snow water equivalent (SWE) in a snowpack. This paper discusses the development of linear regression models based on voltage measurements collected in a field test of a new measuring device that uses a vertical arrangement of capacitive sensors, to predict density, height, and SWE in a snowpack. The data collected grouped into six data sets and analyzed using the software for multivariable analysis Unscrambler X. From the results, three models were selected, one for each parameter. The models have a good prediction performance within the collection of samples. However, the model data sets used in the process do not have good representativity for other sampling conditions.

Keyphrases: capacitive sensor, least square method, model development, snow density

@booklet{EasyChair:4140,
  author    = {Mercedes Noemi Murillo Abril and Beathe Furenes and Nils-Olav Skeie},
  title     = {Development of a Model to Estimate Parameters in a Snowpack Based on Capacitive Measurements},
  howpublished = {EasyChair Preprint 4140},
  year      = {EasyChair, 2020}}