0000000000404331

AUTHOR

Boutheina Ziadi

showing 2 related works from this author

The mapping of the Posidonia oceanica (L.) Delile barrier reef meadow in the southeastern Gulf of Tunis (Tunisia)

2016

Abstract Barrier reefs are among the most important ecomorphosis for Posidonia oceanica meadows and have long been subjected to anthropic pressures. The authors mapped the entire Sidi Rais (northeastern Tunisia) Posidonia oceanica barrier reef by means of remote sensing based on processing a satellite image acquired via Google Earth © software, coupled with field observations obtained by snorkeling. The map thus produced represents the P. oceanica barrier reef in its current state, covering a total area of 156.77 ha, the reef being divided into three distinct sections separated by reverse flows with each section subject to varied anthropic factors and disturbances.

0106 biological sciences010504 meteorology & atmospheric sciencesCymodocea nodosaBarrier reefSnorkeling01 natural sciences[ SDE ] Environmental SciencesSatellite image14. Life underwaterBarrier reef mappingReef0105 earth and related environmental sciencesEarth-Surface ProcessesgeographyCymodocea nodosageography.geographical_feature_categorybiologybusiness.industry010604 marine biology & hydrobiologyPosidonia oceanicaGeologybiology.organism_classificationCurrent (stream)OceanographyRemote sensing (archaeology)Anthropic impactPosidonia oceanica[SDE]Environmental SciencesbusinessGeologyJournal of African Earth Sciences
researchProduct

Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons

2018

Abstract Multivariate trophic indices provide an efficient way to assess and classify the eutrophication level and ecological status of a given water body, but their computation requires the availability of experimental information on many parameters, including biological data, that might not always be available. Here we show that machine learning techniques – once trained against a full data set – can be used to infer plankton biomass information from chemical and physical parameter only, so that trophic index can then be computed without using additional biological data. More specifically, we reconstruct plankton information from chemical and physical data, and this information together w…

0106 biological sciencesGeneral Decision Sciences010501 environmental sciencesMachine learningcomputer.software_genre01 natural sciencesZooplanktonPhytoplankton14. Life underwaterEcology Evolution Behavior and SystematicsComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesTrophic levelBiological dataEcologybusiness.industry010604 marine biology & hydrobiologyPlanktonEcological indicator[SDE]Environmental SciencesEnvironmental scienceArtificial intelligenceTrixbusinessEutrophicationcomputer
researchProduct