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      <p>Please join us for this week's EOAS Colloquium speaker TODAY at
        3pm in EOA 1050:</p>
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            New Roman",serif"><br>
          </span></font></p>
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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>
      <p class="MsoNormal" style="margin-bottom:0in;text-align:center;
        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" moz-do-not-send="true"><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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            Roman",serif"></span></font></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">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>
      <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"> </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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          Roman",serif;mso-bidi-theme-font: minor-bidi"><span style="mso-bidi-font-style:italic"><br>
          </span></span></p>
      <p class="MsoNormal" style="margin-bottom:0in;text-align:justify;text-justify:
        inter-ideograph;line-height:normal;layout-grid-mode:char"> </p>
      <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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