Testing Artificial Neural Network (ANN) for Spatial Interpolation

Kód výsledku v IS VaVaI

RIV/61989592:15310/14:33152902 - isvavai.cz

Výsledek na webu

http://omicsgroup.org/journals/testing-artificial-neural-network-ann-for-spatial-interpolation-2329-6755.1000145.pdf

DOI - Digital Object Identifier

10.4172/2329-6755.1000145

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DE - Zemský magnetismus, geodesie, geografie

OECD FORD obor

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Projekt

EE2.3.30.0041: Podpora vytváření excelentních výzkumných týmů a intersektorální mobility na Univerzitě Palackého v Olomouci II.

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 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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