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    <p>Please join us for this week's EOAS Colloquium speaker Friday Jan
      20 at 3pm in 1050:</p>
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          New Roman",serif"><br>
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    <p class="MsoNormal" style="margin-bottom:0in;text-align:center;
      line-height:normal;layout-grid-mode:char" align="center"><b><span style="font-size:16.0pt;
          font-family:"Times New Roman",serif">Model- and
          Data-Based Approaches for
          Identifying Controlling Processes of an Environmental System
          and my View of Environmental
          Data Science</span></b></p>
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      line-height:normal;layout-grid-mode:char" align="center"><b><span style="font-size:16.0pt;
          font-family:"Times New Roman",serif"> </span></b></p>
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      line-height:normal;layout-grid-mode:char" align="center"><span style="font-size:16.0pt;
        font-family:"Times New Roman",serif">Dr. Ming Ye (</span><span style="font-size:
        16.0pt"><a href="mailto:mye@fsu.edu"><span style="font-family:"Times New Roman",serif">mye@fsu.edu</span></a></span><span style="font-size:16.0pt;font-family:"Times New
        Roman",serif">), Professor</span></p>
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    <p class="MsoNormal" style="margin-bottom:0in;text-align:center;
      line-height:normal;layout-grid-mode:char" align="center"><span style="font-size:12.0pt;
        font-family:"Times New Roman",serif">Department of
        Earth, Ocean, and
        Atmospheric Science and Department of Scientific Computing</span></p>
    <p class="MsoNormal" style="margin-bottom:0in;text-align:center;
      line-height:normal;layout-grid-mode:char" align="center"><span style="font-size:12.0pt;
        font-family:"Times New Roman",serif">Florida State
        University</span></p>
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      inter-ideograph;line-height:normal;layout-grid-mode:char"><span style="font-size:12.0pt;font-family:"Times New
        Roman",serif"> </span></p>
    <p class="MsoNormal" style="margin-bottom:0in;text-align:justify;text-justify:
      inter-ideograph;line-height:normal;layout-grid-mode:char"><span style="font-size:12.0pt;font-family:"Times New
        Roman",serif">An environmental
        system is open and complex, and its evolution in space and time
        involves a
        large number of natural and anthropogenic processes and their
        interactions. It
        is necessary to identify controlling processes and process
        interactions for
        advancing our understanding of the environmental system to
        support
        science-informed decision-making on environmental protection.
        This seminar
        presents both model- and data-based approaches that we have
        recently developed for
        identifying controlling processes. The model-based approaches
        use global
        sensitivity analyses, and have a unique feature of addressing
        uncertainty not
        only in process representations (i.e., process models) but also
        in process
        parameters (i.e., process model parameters). The approaches are
      </span><span style="font-size:12.0pt;font-family:"Times New
        Roman",serif;mso-bidi-theme-font:
        minor-bidi">designed to answer the following question: if we are
        not certain
        about process representations and process parameter values, can
        we identify <span style="mso-bidi-font-style:italic">important
          or influential processes for developing
          and improving environmental system models? The model-based
          approaches are always
          computational expensive, but can be applied to an
          environmental system with
          scarce data. The data-based approaches do not require
          developing process models,
          but use data to identify spatio-temporal patterns and the
          processes that
          control the patterns. This is achieved in our studies by using
          cluster analysis
          (one-way clustering and co-clustering) and </span><i style="mso-bidi-font-style:
          normal">t</i><span style="mso-bidi-font-style:italic">-distributed
          Stochastic
          Neighbor Embedding (t-SNE) methods. An example application
          will be presented
          for a dataset of 13,024 groundwater geochemical measurements
          for 11 geochemical
          parameters of 1,184 groundwater samples collected over 23
          years from 29
          monitoring in a regional aquifer. The model- and data-based
          approaches are an
          integration of environmental science, mathematics and
          statistics, and computer
          science. At the last of the presentation, I will discuss our
          research on study
          sinkhole lakes at Lake Miccosukee and Lake Jackson located in
          Tallahassee. <br>
        </span></span></p>
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    <p class="MsoNormal" style="margin-bottom:0in;text-align:justify;text-justify:
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          Roman",serif;mso-bidi-theme-font:
          minor-bidi;mso-bidi-font-style:italic">The two figures below
          are for
          illustrating the model- and data-based approaches. </span></b></p>
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