Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F21%3A86954" target="_blank" >RIV/60460709:41330/21:86954 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2072-4292/13/3/333" target="_blank" >https://www.mdpi.com/2072-4292/13/3/333</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/rs13030333" target="_blank" >10.3390/rs13030333</a>
Alternative languages
Result language
angličtina
Original language name
Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms
Original language description
Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10501 - Hydrology
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing
ISSN
2072-4292
e-ISSN
2072-4292
Volume of the periodical
13
Issue of the periodical within the volume
3
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
1-23
UT code for WoS article
000615520100001
EID of the result in the Scopus database
2-s2.0-85099780284