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Title:
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EXTRACTION OF HISTORICAL WATER SALINITY FROM DIATOM ALGAE FOSSILS: A DATA MINING APPROACH |
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Author(s):
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Ray R. Hashemi, Jeffery A. Young, Azita A. Bahrami, Nicholas R. Tyler, Jay Y. S. Hodgson |
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ISBN:
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978-989-8533-56-2 |
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Editors:
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Hans Weghorn |
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Year:
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2016 |
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Edition:
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Single |
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Keywords:
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Data Mining, Past Salinity Levels Extraction, Diatom Algae Fossils, Modified Rough Sets, Fuzzy Logic, and Feature Extraction |
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Type:
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Full Paper |
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First Page:
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163 |
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Last Page:
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170 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The salinity of a body of water and climate changes have an intertwined relationship. The discovery of the historical quantified salinity of inland lakes is extremely important to understanding climate change, carbon dioxide levels, and global warming. In this research effort the past salinity levels for Santa Fe Lake located in New Mexico, USA, were discovered by mining the Diatom Algae Fossils data obtained from the bottom of the lake. Modified Rough Sets and Fuzzy logic were employed in mining such data and validity of the findings was also tested which revealed 72% of accuracy for the produced results. |
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