Integration neural networks and GIS in modeling landscape changes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F14%3A33150329" target="_blank" >RIV/61989592:15310/14:33150329 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5593/SGEM2014/B21/S8.084" target="_blank" >http://dx.doi.org/10.5593/SGEM2014/B21/S8.084</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/SGEM2014/B21/S8.084" target="_blank" >10.5593/SGEM2014/B21/S8.084</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Integration neural networks and GIS in modeling landscape changes

Popis výsledku v původním jazyce

Geographical information system is very powerful tool to manage and analyses land use data. The integration of Geographic Information Systems and Artificial Neural Networks offers a mechanism to lower the cost of analysis of landscape change by reducingthe amount of time spent interpreting data. Artificial Neural Networks (ANNs) have been proven to be useful in the interpretation of natural resource information. Back-Propagation Neural Networks are one of the most common and widely used architectures.Many architectures and types of ANNs have been developed, and many of them are PC-based. Change prediction is based on the analysis of the Markov chain. This process determines the condition of the system on the basis of its previous condition and likelihood of changes which have occurred between them. Models of change serve as useful tools for exploring the various mechanisms by which land use change occurs actual projecting and potential future environmental and evaluating the impact.

Název v anglickém jazyce

Integration neural networks and GIS in modeling landscape changes

Popis výsledku anglicky

Geographical information system is very powerful tool to manage and analyses land use data. The integration of Geographic Information Systems and Artificial Neural Networks offers a mechanism to lower the cost of analysis of landscape change by reducingthe amount of time spent interpreting data. Artificial Neural Networks (ANNs) have been proven to be useful in the interpretation of natural resource information. Back-Propagation Neural Networks are one of the most common and widely used architectures.Many architectures and types of ANNs have been developed, and many of them are PC-based. Change prediction is based on the analysis of the Markov chain. This process determines the condition of the system on the basis of its previous condition and likelihood of changes which have occurred between them. Models of change serve as useful tools for exploring the various mechanisms by which land use change occurs actual projecting and potential future environmental and evaluating the impact.

Druh

D - Stať ve sborníku

CEP obor

DE - Zemský magnetismus, geodesie, geografie

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.20.0170" target="_blank" >EE2.3.20.0170: Budování výzkumně-vzdělávacího týmu v oblasti modelování přírodních jevů a využití geoinformačních systémů, s vazbou na zapojení do mezinárodních sítí a programů.</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

14th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing

ISBN

978-619-7105-10-0

ISSN

1314-2704

e-ISSN

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Počet stran výsledku

9

Strana od-do

651-658

Název nakladatele

STEF92 Technology Ltd.

Místo vydání

Sofia

Místo konání akce

Albena

Datum konání akce

17. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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