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Classification tools based on artificial neural networks for the purpose of identification of origin of organic matter and oil pollution in recent sediments
The distinction between autochthonous, and oil-like origin of organic matter in geological sediments can be performed on the basis of n-alkane abundance and distribution patterns, determined by gas chromatography, or on the basis of the carbon-isotope ratio (δC13PDB) patterns of dominant n-alkanes, determined by gas chromatography-mass spectroscopy. Here we present solutions for automatic classification of organic matter origin in geological sediments, based on artificial neural networks.
Keywords:
Sediments / Oil-type pollution / n-alkane distribution / Carbon isotope ratio / Artificial neural networks
TY - JOUR
AU - Mićić, M.
AU - Jovančićević, B.
AU - Polić, P.
AU - Šušić, Nenad
AU - Marković, D.
PY - 1998
UR - http://rims.institutims.rs/handle/123456789/13
AB - The distinction between autochthonous, and oil-like origin of organic matter in geological sediments can be performed on the basis of n-alkane abundance and distribution patterns, determined by gas chromatography, or on the basis of the carbon-isotope ratio (δC13PDB) patterns of dominant n-alkanes, determined by gas chromatography-mass spectroscopy. Here we present solutions for automatic classification of organic matter origin in geological sediments, based on artificial neural networks.
PB - TU Munchen
T2 - Fresenius Environmental Bulletin
T1 - Classification tools based on artificial neural networks for the purpose of identification of origin of organic matter and oil pollution in recent sediments
EP - 653
IS - 11-12
SP - 648
VL - 7
UR - https://hdl.handle.net/21.15107/rcub_rims_13
ER -
@article{
author = "Mićić, M. and Jovančićević, B. and Polić, P. and Šušić, Nenad and Marković, D.",
year = "1998",
abstract = "The distinction between autochthonous, and oil-like origin of organic matter in geological sediments can be performed on the basis of n-alkane abundance and distribution patterns, determined by gas chromatography, or on the basis of the carbon-isotope ratio (δC13PDB) patterns of dominant n-alkanes, determined by gas chromatography-mass spectroscopy. Here we present solutions for automatic classification of organic matter origin in geological sediments, based on artificial neural networks.",
publisher = "TU Munchen",
journal = "Fresenius Environmental Bulletin",
title = "Classification tools based on artificial neural networks for the purpose of identification of origin of organic matter and oil pollution in recent sediments",
pages = "653-648",
number = "11-12",
volume = "7",
url = "https://hdl.handle.net/21.15107/rcub_rims_13"
}
Mićić, M., Jovančićević, B., Polić, P., Šušić, N.,& Marković, D.. (1998). Classification tools based on artificial neural networks for the purpose of identification of origin of organic matter and oil pollution in recent sediments. in Fresenius Environmental Bulletin
TU Munchen., 7(11-12), 648-653.
https://hdl.handle.net/21.15107/rcub_rims_13
Mićić M, Jovančićević B, Polić P, Šušić N, Marković D. Classification tools based on artificial neural networks for the purpose of identification of origin of organic matter and oil pollution in recent sediments. in Fresenius Environmental Bulletin. 1998;7(11-12):648-653.
https://hdl.handle.net/21.15107/rcub_rims_13 .
Mićić, M., Jovančićević, B., Polić, P., Šušić, Nenad, Marković, D., "Classification tools based on artificial neural networks for the purpose of identification of origin of organic matter and oil pollution in recent sediments" in Fresenius Environmental Bulletin, 7, no. 11-12 (1998):648-653,
https://hdl.handle.net/21.15107/rcub_rims_13 .
