<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>850</id>
    <completedYear/>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>53</pageFirst>
    <pageLast>64</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Shaker</publisherName>
    <publisherPlace>Aachen</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2003-06-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data Preprocessing for Hydrological and Hydromorphological Studies using Neural Networks</title>
    <abstract language="eng">The use of artificial neural networks for various hydrologic and hydromorphological studies is being investigated. Application of the Artificial Intelligence (AI) methods, especially the neural networks for water related studies is expanding lately, due to its advantages such as being less subjected to he constraints of the physical considerations and quick delivery of feasible and cost-effective responses. Primary stage results of the research are presented in two separate examples.The first example is the runoff modelling for the case of Yellowstone River, USA at the outlet of a high-altitude lake using neural networks, in which the influence of melting ice and snow cover is carefully considered. A wavelet transform is used for smoothening the input signal for the neural network model in order to improve the accuracy and prevent the inconsistency of neural network solutions. A morphological evolution study along cross-shore profiles at the Kiel Bay, Baltic Sea coast using ANN provides the second case. Data preprocessing or in this particular case downsampling of bathymetry measurements through a number of cross shore profiles was done by a wavelet transform.The data oriented approaches, such as neural networks often have to deal with the abundant data or noisy observations, which normally require a thorough analysis, preprocessing or downsampling, to enable a satisfactory performance of the models. The above case studies emphasize the importance and necessity of the data analysis and preprocessing.</abstract>
    <parentTitle language="deu">Simulation in Umwelt- und Geowissenschaften, Workshop Cottbus 2002</parentTitle>
    <identifier type="isbn">3-8322-0733-3</identifier>
    <enrichment key="UBICOIdent">001160</enrichment>
    <enrichment key="UBICOseries">ASIM-Mitteilungen ; 79</enrichment>
    <enrichment key="UBICOseries">Berichte aus der Umweltinformatik</enrichment>
    <author>
      <firstName>Bunchingiv</firstName>
      <lastName>Bazartseren</lastName>
    </author>
    <submitter>
      <firstName>...</firstName>
      <lastName>Administrator</lastName>
    </submitter>
    <author>
      <firstName>Klaus-Peter</firstName>
      <lastName>Holz</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>coastal morphological study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hydrological studies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>hydromorphological studies</value>
    </subject>
    <collection role="old_institute" number="02005">LS Bauinformatik</collection>
  </doc>
</export-example>
