<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>23807</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>181</pageFirst>
    <pageLast>195</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>17</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-03-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity</title>
    <abstract language="eng">Soil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estimate soil electrical conductivity (σ) and soil moisture. However, obtaining the σ − θ relationship is not straightforward due to the non-linearity and also dependency on many different soil and environmental properties. The purpose of this paper is to determine if artificial neural network is an appropriate machine learning technique for relating electrical conductivity to soil water content. In this respect, time-lapse electrical resistivity tomography measurements were carried out along a transect in the Chicken Creek catchment (Brandenburg, Germany). To ensure proper retrieval of the σ and θ, reference values were measured near the beginning of the transect via an excavated pit using 5TE capacitance sensors installed at different depths. We explored robustness and pertinence of the artificial neural network approach in comparison with Rhoades model (as a commonly used petrophysical relationship) to convert the inversely estimated σ from electrical resistivity tomography to the θ. The proposed approach was successfully validated and benchmarked by comparing the estimated values with the reference data. This study showed the superiority of the artificial neural network approach to the Rhoades model to obtain σ − θ relationship. In particular, artificial neural network allowed for more accurate estimation of the temporal wetting front than the petrophysical model. The proposed methodology thus offers a great promise for deriving spatiotemporal soil moisture patterns from geophysical data and obtaining the in situ σ − θ relationship, taking into account the non-linear variations of the soil moisture.</abstract>
    <parentTitle language="eng">Near Surface Geophysics</parentTitle>
    <identifier type="doi">10.1002/nsg.12036</identifier>
    <identifier type="issn">1569-4445</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Davood</firstName>
      <lastName>Moghadas</lastName>
    </author>
    <submitter>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </submitter>
    <author>
      <firstName>Annika</firstName>
      <lastName>Badorreck</lastName>
    </author>
    <collection role="institutes" number="2052">Zentrum für Landschaftstransformation (ZLT)</collection>
  </doc>
  <doc>
    <id>26528</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_noref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-12-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Soil Moisture Patterns in an artificial water catchment – A machine learning approach from geophysical measurements</title>
    <parentTitle language="eng">EGU General Assembly 2020, Online, 4–8 May 2020</parentTitle>
    <identifier type="doi">10.5194/egusphere-egu2020-2652</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">EGU2020-2652</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <author>
      <firstName>Davood</firstName>
      <lastName>Moghadas</lastName>
    </author>
    <submitter>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </submitter>
    <author>
      <firstName>Annika</firstName>
      <lastName>Badorreck</lastName>
    </author>
    <collection role="institutes" number="2052">Zentrum für Landschaftstransformation (ZLT)</collection>
  </doc>
  <doc>
    <id>26527</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_noref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-12-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Characterization of soil electrical conductivity from Chicken Creek Catchment using deep learning inversion of geophysical data</title>
    <parentTitle language="eng">EGU General Assembly 2020, Online, 4–8 May 2020</parentTitle>
    <identifier type="doi">10.5194/egusphere-egu2020-2664</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">EGU2020-2664</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <author>
      <firstName>Davood</firstName>
      <lastName>Moghadas</lastName>
    </author>
    <submitter>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </submitter>
    <author>
      <firstName>Annika</firstName>
      <lastName>Badorreck</lastName>
    </author>
    <collection role="institutes" number="2052">Zentrum für Landschaftstransformation (ZLT)</collection>
  </doc>
  <doc>
    <id>24186</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>671</pageFirst>
    <pageLast>684</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>12</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-06-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A web-based platform for terrestrial data repository from Chicken Creek catchment</title>
    <abstract language="eng">Exploring hydrological and ecological processes plays a key role in understanding ecosystem developments. In this respect, the constructed catchment, Chicken Creek, has been established for fundamental and interdisciplinary scientific research. Since 2005, an ongoing monitoring program has been launched to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the site. This comprehensive and multidisciplinary monitoring program has produced a diverse large data set. Handling such complex data for research purposes can be a cumbersome task. Consequently, we developed an online data portal (https://www.b-tu.de/chicken-creek/apps/datenportal/) to efficiently handle the data from Chicken Creek catchment. The portal was constructed using Shiny package of the R programming language. This platform provides a web-based data repository allowing for data discovery, download, visualization, and analysis. The data include time series of different parameters from installed sensors, data from laboratory analyses, vegetation data, data from sampling campaigns, and aerial photos. This platform demonstrates the relevancy and potentiality of the R-Shiny for constructing an online data portal to be used for multidisciplinary scientific purposes. The Chicken Creek&#13;
data portal thus provides a comprehensive and reliable database to give scientists a fast and easy access to all collected data.</abstract>
    <parentTitle language="eng">Earth Science Informatics</parentTitle>
    <identifier type="doi">10.1007/s12145-019-00385-0</identifier>
    <identifier type="issn">1865-0481</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Davood</firstName>
      <lastName>Moghadas</lastName>
    </author>
    <submitter>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </submitter>
    <author>
      <firstName>Wolfgang</firstName>
      <lastName>Schaaf</lastName>
    </author>
    <author>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </author>
    <author>
      <firstName>Annika</firstName>
      <lastName>Badorreck</lastName>
    </author>
    <author>
      <firstName>Reinhard F.</firstName>
      <lastName>Hüttl</lastName>
    </author>
    <collection role="institutes" number="2052">Zentrum für Landschaftstransformation (ZLT)</collection>
    <collection role="institutes" number="2402H01">FG Bodenschutz und Rekultivierung (ehemals)</collection>
  </doc>
  <doc>
    <id>23950</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>1</pageNumber>
    <edition/>
    <issue/>
    <volume>21</volume>
    <type>conferenceobject_noref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-04-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Long-term monitoring program at the constructed catchment "Chicken Creek"</title>
    <abstract language="eng">Ecosystems are characterized by high complexity and are dynamically changing by abiotic and biotic drivers such as climate, vegetation and soil fauna. Due to feedback processes between compartments, ecosystems show an inherent degree of locally heterogeneous properties and structures at multiple spatial and temporal scales.&#13;
The constructed catchment “Chicken Creek” represents a unique and outstanding site to study an ecosystem at the initial stage of development. The catchment is located about 20 km south of the city of Cottbus (Germany). The 6-ha catchment area was built in 2005 of coarse-textured quaternary sediments from the adjacent lignite mine and forms a back- and foot-slope that flattens out to a pond. A clay liner of 2-3 m thickness seals the catchment at the bottom, allowing for the formation of a local water body fed by precipitation. After construction the site was left to an unrestricted and unmanaged succession to elucidate the role of structures und processes during the initial establishment of an ecosystem. To study processes of ecosystem development and newly emerging structures the Chicken Creek catchment is equipped with a comprehensive ecological monitoring network. A network of more than 40 wells and two weirs allow for recording groundwater levels, surface runoff and discharge. Meteorological parameters are recorded at three weather stations. Annual aerial photos and vegetation monitoring reveal the floral colonisation of the area. Here, we present the 12 years of diverse and extensive monitoring program which has been launched to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the catchment area.</abstract>
    <parentTitle language="eng">Geophysical Research Abstracts</parentTitle>
    <identifier type="url">https://meetingorganizer.copernicus.org/EGU2019/EGU2019-5038.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">EGU2019-5038</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Annika</firstName>
      <lastName>Badorreck</lastName>
    </author>
    <submitter>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </submitter>
    <author>
      <firstName>Wolfgang</firstName>
      <lastName>Schaaf</lastName>
    </author>
    <author>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </author>
    <author>
      <firstName>Davood</firstName>
      <lastName>Moghadas</lastName>
    </author>
    <author>
      <firstName>Reinhard F.</firstName>
      <lastName>Hüttl</lastName>
    </author>
    <collection role="institutes" number="2052">Zentrum für Landschaftstransformation (ZLT)</collection>
    <collection role="institutes" number="2402H01">FG Bodenschutz und Rekultivierung (ehemals)</collection>
  </doc>
  <doc>
    <id>23954</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>1</pageNumber>
    <edition/>
    <issue/>
    <volume>21</volume>
    <type>conferenceobject_noref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-04-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Chicken Creek data portal: a web-based R-Shiny platform for terrestrial data repository</title>
    <abstract language="eng">Exploring hydrological and ecological processes plays a key role in understanding ecosystem development. In this respect, the constructed catchment, Chicken Creek (Hühnerwasser), has been established to promote fundamental and interdisciplinary scientific research. Since 2005, an on-going monitoring program has been established in this catchment to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the site. This comprehensive and multidisciplinary monitoring program has produced a large and diverse data set. Managing and exploring such a complex data set for research purposes can be a cumbersome task. As a consequence, we developed an online data portal https://www.b-tu.de/chicken-creek/apps/datenportal/ to efficiently handle the data from Chicken Creek catchment. The portal was constructed using R programming language, Shiny package, and the accompanying local Linux server. This platform allows for efficient data discovery, download, visualization, and analysis. Data visualizations are freely available, while data storage is limited to the authorized users. In comparison with the commonly used data base tools, R-Shiny offers several advantages. As an open source package, this platform allows for constructing a web data platform in an interactive way taking into account the complexity and diversity of the data. The flexibility of this routine enables to make an efficient user-demand data portal rather than relying on predetermined outputs. It also offers flexibility in data handling by using many different R packages, rendering versatile and extensive functionalities. R-Shiny is particularly powerful in terms of statistical analyses. Although the Chicken Creek online data portal is complete and available, new features, and extended capabilities are under active development. The Chicken Creek data portal provides a comprehensive and reliable database to give scientists a fast and easy access to all collected data.&#13;
Consequently, R-Shiny offers a great potential for future development of the web-based data portals to efficiently handle scientific data collected at different scales.</abstract>
    <parentTitle language="eng">Geophysical Research Abstracts</parentTitle>
    <identifier type="url">https://meetingorganizer.copernicus.org/EGU2019/EGU2019-3270.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">EGU2019-3270</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Davood</firstName>
      <lastName>Moghadas</lastName>
    </author>
    <submitter>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </submitter>
    <author>
      <firstName>Wolfgang</firstName>
      <lastName>Schaaf</lastName>
    </author>
    <author>
      <firstName>Werner</firstName>
      <lastName>Gerwin</lastName>
    </author>
    <author>
      <firstName>Annika</firstName>
      <lastName>Badorreck</lastName>
    </author>
    <author>
      <firstName>Reinhard F.</firstName>
      <lastName>Hüttl</lastName>
    </author>
    <collection role="institutes" number="2052">Zentrum für Landschaftstransformation (ZLT)</collection>
    <collection role="institutes" number="2402H01">FG Bodenschutz und Rekultivierung (ehemals)</collection>
  </doc>
</export-example>
