<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>1755</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>95</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-06-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2023-04-21</thesisDateAccepted>
    <title language="eng">Real-Time Web Application for Decision Support in Groundwater Management</title>
    <abstract language="eng">Groundwater is an essential resource that is used for a wide range of purposes all over the world, and retaining this important natural asset demands that it be managed and utilized sustainably. Despite the fact that groundwater is frequently abundant and widely utilized, it is difficult and costly to precisely estimate water levels when compared to surface water systems. Groundwater managers require improved digital tools to understand the system state of the aquifers they seek to interact with sustainably (e.g., accessing real-time updates on the state of the water table to check whether&#13;
pumping operation is achieving or violating a desired drawdown target.) Here we propose a workflow for a pilot region that (a) temporally interpolates irregularly, manually-measured water levels in a real observation network using data from neighboring sensor-equipped observation, using a multiple linear regression approach to&#13;
reduce complexity. The resulting interpolated time series are then used in conjunction with the sensor data to come up with (b) spatial interpolations of the groundwater field over time using inversed distance weighting (IDW) at any given point in time. From&#13;
that, spatial estimates of the groundwater field can be transformed into maps of deviation from drawdown targets. We visualize these targets and their temporal evolution for groundwater managers in real-time on a web application. The web application displays these maps together with operational data (e.g., extraction rates)&#13;
and meteorological data (recent and forecasted precipitation rates). It serves as a foundational tool for groundwater management when making control decisions to save energy in climate change mitigation and to reduce costs.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-17550</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>CC BY-NC-SA 4.0 International - Namensnennung-Nicht kommerziell-Weitergabe unter gleichen Bedingungen</licence>
    <author>Sina Navid</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Groundwater</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Web application</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plotly Dash</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data Engineering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data visualizing</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/1755/SinaNavid_master_thesis.pdf</file>
  </doc>
</export-example>
