Global scale massive feature extraction from monthly hydroclimatic time series Statistical characterizations, spatial patterns and hydrological similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F21%3A86949" target="_blank" >RIV/60460709:41330/21:86949 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0048969720381432?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0048969720381432?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scitotenv.2020.144612" target="_blank" >10.1016/j.scitotenv.2020.144612</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Global scale massive feature extraction from monthly hydroclimatic time series Statistical characterizations, spatial patterns and hydrological similarity

Popis výsledku v původním jazyce

Hydroclimatic time series analysis focuses on a few feature types which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results here we approach hydroclimatic time series analysis differently, by performing massive feature extraction. In this respect, we develop a big data framework for hydroclimatic variable behaviour characterization. This framework relies on approximately 60 diverse features and is completely automatic (in the sense that it does not depend on the hydroclimatic process at hand). We apply the new framework to characterize mean monthly temperature, total monthly precipitation and mean monthly river flow. The applications are conducted at the global scale by exploiting 40 year long time series originating from over 13 000 stations. We extract interpretable knowledge on seasonality, trends, autocorrelation, long range dependence and entropy, and on feature ty

Název v anglickém jazyce

Global scale massive feature extraction from monthly hydroclimatic time series Statistical characterizations, spatial patterns and hydrological similarity

Popis výsledku anglicky

Hydroclimatic time series analysis focuses on a few feature types which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results here we approach hydroclimatic time series analysis differently, by performing massive feature extraction. In this respect, we develop a big data framework for hydroclimatic variable behaviour characterization. This framework relies on approximately 60 diverse features and is completely automatic (in the sense that it does not depend on the hydroclimatic process at hand). We apply the new framework to characterize mean monthly temperature, total monthly precipitation and mean monthly river flow. The applications are conducted at the global scale by exploiting 40 year long time series originating from over 13 000 stations. We extract interpretable knowledge on seasonality, trends, autocorrelation, long range dependence and entropy, and on feature ty

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Science of the Total Environment

ISSN

0048-9697

e-ISSN

1879-1026

Svazek periodika

767

Číslo periodika v rámci svazku

144612

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

68

Strana od-do

1-68

Kód UT WoS článku

000617681100051

EID výsledku v databázi Scopus

2-s2.0-85099361084