The Tasmanian Marine Analysis Network (TasMAN)

Timms, Greg; McCulloch, John; McCarthy, Pete; Howell, Ben; Souza, P. A. de; Dunbabin, Matthew; Hartmann, Klaas

doi:10.1109/oceanse.2009.5278177

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…The above described methodology has been applied to both temperature and conductivity marine sensor data collected from the Sullivan's Cove node of the Tasmanian Marine Analysis Network (TasMAN) 23 …”

Section: Resultsmentioning

confidence: 99%

Short-Term Prediction of Marine Sensor Data with Fuzzy Clustering

O'Mara

Shahriar

2015

Int. J. Patt. Recogn. Artif. Intell.

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In predicting water quality variables in the short term, a novel technique using fuzzy pattern similarity-based fuzzy clustering has been proposed. The experimental results show that the proposed method outperforms than existing similar methods for sea water temperature and conductivity data sets from a marine sensor network for environmental monitoring. The shortterm prediction of water quality variables has immense bene¯t in aquaculture and¯sheries industries for decision-making purposes.

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Section: Resultsmentioning

confidence: 99%

Short-Term Prediction of Marine Sensor Data with Fuzzy Clustering

O'Mara

Shahriar

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…The Derwent estuary has suffered significantly from urban stormwater and sewage inflow, industrial pollution and agricultural runoff [1], [2]. Significant progress has been made in recent years to improve water quality but the riverbed sediments still contain very high concentrations of heavy metals, derived from past activities of local heavy industries, including a zinc smelter and a newsprint mill.…”

Section: Introductionmentioning

confidence: 99%

Identifying prototype states within hydrodynamic model outputs using a self-organizing feature map

Williams

Souza

Jones

et al. 2012

2012 Oceans - Yeosu

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The Coastal Environmental Modelling Team at CSIRO Marine and Atmospheric Research, in Hobart, Tasmania, Australia, has been modelling hydrodynamic conditions within the estuarine environment of south-eastern Tasmania for several years. Historical model output has been analysed in an effort to identify prototype hydrodynamic states (i.e., frequently encountered typical hydrodynamic situations) exhibited by the estuarine environment over that period. A competitive-learning neural network, the Self-Organizing Feature Map (SOM), was used to identify these prototype states. Once such a network has been trained, each node in its output layer represents a particular pattern in the input data and nodes representing similar patterns are located near to each other on the two-dimensional output grid, while those representing dissimilar patterns are further apart. Estimated daily average surface hydrodynamic conditions (salinity, temperature and ocean current components) within the south-east Tasmanian estuarine environment, from August 2009 to August 2010, were derived from output provided by the hydrodynamic model. The current components were then analysed using a SOM and subsequent inspection of the SOM output grid enabled a number of prototypical hydrodynamic states to be identified within the model outputs.

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“…Marine sensor data come from sensor networks deployed in a marine environment [ 7 , 8 ]. Types of marine environments include estuaries, seas and oceans.…”

Section: Introductionmentioning

confidence: 99%

Smart Query Answering for Marine Sensor Data

Shahriar

Souza

Timms

2011

Sensors

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We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks.

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The Tasmanian Marine Analysis Network (TasMAN)

Cited by 17 publications

References 11 publications

Short-Term Prediction of Marine Sensor Data with Fuzzy Clustering

Short-Term Prediction of Marine Sensor Data with Fuzzy Clustering

Identifying prototype states within hydrodynamic model outputs using a self-organizing feature map

Smart Query Answering for Marine Sensor Data

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