A Genetic Programming Approach to System Identification of Rainfall-Runoff Models
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F17%3A74734" target="_blank" >RIV/60460709:41330/17:74734 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1007/s11269-017-1719-1" target="_blank" >http://dx.doi.org/10.1007/s11269-017-1719-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11269-017-1719-1" target="_blank" >10.1007/s11269-017-1719-1</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Genetic Programming Approach to System Identification of Rainfall-Runoff Models
Popis výsledku v původním jazyce
Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for futur
Název v anglickém jazyce
A Genetic Programming Approach to System Identification of Rainfall-Runoff Models
Popis výsledku anglicky
Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity, motivate the use of machine learning to build hydrological models. Increasing global climate change and urbanization call for better understanding of altered rainfall-runoff processes. There is a requirement that models are intelligible estimates of underlying physics, coupling explanatory and predictive components, maintaining parsimony and accuracy. Genetic Programming, an evolutionary computation technique has been used for short-term prediction and forecast in the field of hydrology. Advancing data science in hydrology can be achieved by tapping the full potential of GP in defining an evolutionary flexible modelling framework that balances prior information, simulation accuracy and strategy for futur
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10501 - Hydrology
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
WATER RESOURCES MANAGEMENT
ISSN
0920-4741
e-ISSN
—
Svazek periodika
31
Číslo periodika v rámci svazku
12
Stát vydavatele periodika
CZ - Česká republika
Počet stran výsledku
18
Strana od-do
3975-3992
Kód UT WoS článku
000407831200017
EID výsledku v databázi Scopus
2-s2.0-85021845733