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AI can provide a data-driven approach to analyzing ocean data. DFO has developed a predictive model to sift through the data piles of ocean data to find (dis-)similarities between multidimensional profiles of oceanographic data. '''The insights gained from the model can be used to answer any questions about dynamic changes in our oceans.'''

AI can provide a data-driven approach to analyzing ocean data. Department of Fisheries and Oceans (DFO) has developed a predictive model to sift through the data piles of ocean data to find (dis-)similarities between multidimensional profiles of oceanographic data. '''The insights gained from the model can be used to answer any questions about dynamic changes in our oceans.'''

    

    

    

    

[[File:Ocean data.png|thumb|338x338px|<small>'''How Ocean Data is Collected?'''</small> <ref>https://argo.ucsd.edu/about/</ref>]]

[[File:Ocean data.png|thumb|338x338px|<small>'''How Ocean Data is Collected?'''</small> <ref>https://argo.ucsd.edu/about/</ref>]]

The Department of Fisheries and Oceans (DFO) Canada has been surveying Canada’s oceans to monitor the evolution of Canada’s oceans, as well as to perform scientific research. The department frequently collects ocean observations using in situ measurements. Ocean data is considered multidimensional data where ocean observations are collected at different depths of the ocean. The amount of ocean data and data dimensions is rising sharply. Scientists tried to use simulations to simulate the ocean environment. However''', ocean simulation models don’t reflect the complex relationship between the different ocean observations. Moreover, current traditional ocean data analysis mostly uses manual classification and recognition. This can be resource-intensive, time-consuming, and requires a specific kind of expertise.'''

DFO Canada has been surveying Canada’s oceans to monitor the evolution of Canada’s oceans, as well as to perform scientific research. The department frequently collects ocean observations using in situ measurements. Ocean data is considered multidimensional data where ocean observations are collected at different depths of the ocean. The amount of ocean data and data dimensions is rising sharply. Scientists tried to use simulations to simulate the ocean environment. However''', ocean simulation models don’t reflect the complex relationship between the different ocean observations. Moreover, current traditional ocean data analysis mostly uses manual classification and recognition. This can be resource-intensive, time-consuming, and requires a specific kind of expertise.'''