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Multitask learning with statistical parametrization for ecohydrological analysis. / Леменкова, Полина Алексеевна.

в: Transylvanian Review of Systematical and Ecological Research, 04.07.2025, стр. 1-20.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

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Леменкова, Полина Алексеевна. / Multitask learning with statistical parametrization for ecohydrological analysis. в: Transylvanian Review of Systematical and Ecological Research. 2025 ; стр. 1-20.

BibTeX

@article{a049cd660e27407faa06210c08bd2416,
title = "Multitask learning with statistical parametrization for ecohydrological analysis",
abstract = "This paper proposes a novel multi-task statistical learning framework which aims to concurrently address all the environmental challenges in the Alps. The goal is to analyse the effects of lichen and fog on water balance. The objective is the analysis of water balance mechanisms by investigating the contribution of fog and the role of forest age in the water cycle. The methods include advanced multitask learning with statistical modelling techniques. The results shown that interception plays a dominant role in the precipitation and evapotranspiration partitioning, enhanced by lichens. Trees transpiration as lower in the young stand and the evapotranspiration of soil and understory contributed considerably to the water balance at both stands. Moreover, fog caused additional throughfall in mixed fog and rain precipitation. ",
keywords = "Alps, monitoring, multitask learning, statistical framework, Python, R language",
author = "Леменкова, {Полина Алексеевна}",
year = "2025",
month = jul,
day = "4",
doi = "10.5281/zenodo.15805003",
language = "English",
pages = "1--20",
journal = "Transylvanian Review of Systematical and Ecological Research",
issn = "2344-3219",

}

RIS

TY - JOUR

T1 - Multitask learning with statistical parametrization for ecohydrological analysis

AU - Леменкова, Полина Алексеевна

PY - 2025/7/4

Y1 - 2025/7/4

N2 - This paper proposes a novel multi-task statistical learning framework which aims to concurrently address all the environmental challenges in the Alps. The goal is to analyse the effects of lichen and fog on water balance. The objective is the analysis of water balance mechanisms by investigating the contribution of fog and the role of forest age in the water cycle. The methods include advanced multitask learning with statistical modelling techniques. The results shown that interception plays a dominant role in the precipitation and evapotranspiration partitioning, enhanced by lichens. Trees transpiration as lower in the young stand and the evapotranspiration of soil and understory contributed considerably to the water balance at both stands. Moreover, fog caused additional throughfall in mixed fog and rain precipitation.

AB - This paper proposes a novel multi-task statistical learning framework which aims to concurrently address all the environmental challenges in the Alps. The goal is to analyse the effects of lichen and fog on water balance. The objective is the analysis of water balance mechanisms by investigating the contribution of fog and the role of forest age in the water cycle. The methods include advanced multitask learning with statistical modelling techniques. The results shown that interception plays a dominant role in the precipitation and evapotranspiration partitioning, enhanced by lichens. Trees transpiration as lower in the young stand and the evapotranspiration of soil and understory contributed considerably to the water balance at both stands. Moreover, fog caused additional throughfall in mixed fog and rain precipitation.

KW - Alps

KW - monitoring

KW - multitask learning

KW - statistical framework

KW - Python

KW - R language

U2 - 10.5281/zenodo.15805003

DO - 10.5281/zenodo.15805003

M3 - Article

SP - 1

EP - 20

JO - Transylvanian Review of Systematical and Ecological Research

JF - Transylvanian Review of Systematical and Ecological Research

SN - 2344-3219

ER -

ID: 137894205