Testing Artificial Neural Network (ANN) for Spatial Interpolation
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F14%3A33152902" target="_blank" >RIV/61989592:15310/14:33152902 - isvavai.cz</a>
Výsledek na webu
<a href="http://omicsgroup.org/journals/testing-artificial-neural-network-ann-for-spatial-interpolation-2329-6755.1000145.pdf" target="_blank" >http://omicsgroup.org/journals/testing-artificial-neural-network-ann-for-spatial-interpolation-2329-6755.1000145.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4172/2329-6755.1000145" target="_blank" >10.4172/2329-6755.1000145</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Testing Artificial Neural Network (ANN) for Spatial Interpolation
Popis výsledku v původním jazyce
The aim of this research is to test Artificial Neural Network (ANN) package in GRASS 6.4 software for spatial interpolation and to compare it with common interpolation techniques IDW and ordinary kriging. This package was also compared with neural networks packages Nnet and Neuralnet available in software R Project. The entire packages uses multi-layer perceptron (MLP) model trained with the back propagation algorithm. Evaluation methods were based mainly on RMSE. All the tests were done on artificial data created in R Project software; which simulated three surfaces with different characteristics. In order to find the best configuration for the multilayer perceptron many different settings of network were tested (test-and-trial method). The number ofneurons in hidden layers was the main tested parameter. Results indicate that MLP model in the ANN module implemented in GRASS can be used for spatial interpolation purposes. However the resulting RMSE was higher than RMSE from IDW and or
Název v anglickém jazyce
Testing Artificial Neural Network (ANN) for Spatial Interpolation
Popis výsledku anglicky
The aim of this research is to test Artificial Neural Network (ANN) package in GRASS 6.4 software for spatial interpolation and to compare it with common interpolation techniques IDW and ordinary kriging. This package was also compared with neural networks packages Nnet and Neuralnet available in software R Project. The entire packages uses multi-layer perceptron (MLP) model trained with the back propagation algorithm. Evaluation methods were based mainly on RMSE. All the tests were done on artificial data created in R Project software; which simulated three surfaces with different characteristics. In order to find the best configuration for the multilayer perceptron many different settings of network were tested (test-and-trial method). The number ofneurons in hidden layers was the main tested parameter. Results indicate that MLP model in the ANN module implemented in GRASS can be used for spatial interpolation purposes. However the resulting RMSE was higher than RMSE from IDW and or
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
DE - Zemský magnetismus, geodesie, geografie
OECD FORD obor
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Návaznosti výsledku
Projekt
<a href="/cs/project/EE2.3.30.0041" target="_blank" >EE2.3.30.0041: Podpora vytváření excelentních výzkumných týmů a intersektorální mobility na Univerzitě Palackého v Olomouci II.</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2014
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Geology & Geosciences
ISSN
2329-6755
e-ISSN
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Svazek periodika
3
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
9
Strana od-do
1-9
Kód UT WoS článku
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EID výsledku v databázi Scopus
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