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• W2889590147 abstract "We deal with the problem of detecting seagrass presence/absence and distinguishing seagrass families in the Mediterranean via supervised learning methods. By merging datasets about seagrass presence and other external environmental variables, we develop suitable training data, enhanced by seagrass absence data algorithmically produced based on certain hypotheses. Experiments comparing several popular classification algorithms yield up to 93.4% accuracy in detecting seagrass presence. In a feature strength analysis, the most important variables determining presence–absence are found to be Chlorophyll-α levels and Distance-to-Coast. For determining family, variables cannot be easily singled out; several different variables seem to be of importance, with Chlorophyll-α surpassing all others. In both problems, tree-based classification algorithms perform better than others, with Random Forest being the most effective. Hidden preferences reveal that Cymodocea and Posidonia favor the low, limited-range chlorophyll-α levels (<0.5 mg/m3), Halophila tolerates higher salinities (>39), while Ruppia prefers euryhaline conditions (37.5–39)." @default.
• W2889590147 created "2018-09-27" @default.
• W2889590147 creator A5057537846 @default.
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• W2889590147 date "2018-11-01" @default.
• W2889590147 modified "2023-10-04" @default.
• W2889590147 title "Seagrass detection in the mediterranean: A supervised learning approach" @default.
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• W2889590147 doi "https://doi.org/10.1016/j.ecoinf.2018.09.004" @default.
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