Geodata Scale Restriction using Genetic Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F14%3A33148621" target="_blank" >RIV/61989592:15310/14:33148621 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_20" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01781-5_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_20" target="_blank" >10.1007/978-3-319-01781-5_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Geodata Scale Restriction using Genetic Algorithm

Popis výsledku v původním jazyce

With recent advances in computer sciences (including geosciences) it is possible to combine various methods for geodata processing. There are many methods established for geodata scale restriction, but none of these take into account the concept of information entropy. Our research focused on using genetic algorithm that calculates information entropy in order to set an optimal number of intervals from original non-restricted geodata. We used fitness function by minimizing information entropy loss and we compared the results with commonly used classification method in geosciences. We propose an experimental method that provides promising approach for geodata scale restriction and consequent proper visualization, which is very important for geographicalphenomena interpretation

Název v anglickém jazyce

Geodata Scale Restriction using Genetic Algorithm

Popis výsledku anglicky

With recent advances in computer sciences (including geosciences) it is possible to combine various methods for geodata processing. There are many methods established for geodata scale restriction, but none of these take into account the concept of information entropy. Our research focused on using genetic algorithm that calculates information entropy in order to set an optimal number of intervals from original non-restricted geodata. We used fitness function by minimizing information entropy loss and we compared the results with commonly used classification method in geosciences. We propose an experimental method that provides promising approach for geodata scale restriction and consequent proper visualization, which is very important for geographicalphenomena interpretation

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Innovations in Bio-inspired Computing and Applications

ISBN

978-3-319-01780-8

Počet stran výsledku

9

Strana od-do

215-223

Počet stran knihy

306

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Kód UT WoS kapitoly

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