Regionalization of Indiana Watersheds for Flood Flow Predictions Phase I: Studies in Regionalization of Indiana Watersheds

Details

• Creators:
  Rao, A. Ramachandra
• Corporate Creators:
  Purdue University. Joint Transportation Research Program
• Corporate Contributors:
  Indiana Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Regional planning
  • Watersheds
  • Floods
  • Scaling factor
  • Cluster analysis
  • Mathematical prediction
  • Data analysis
• Publication/ Report Number:
  FHWA/IN/JTRP-2002/2
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Indiana
• Edition:
  Final Report
• Corporate Publisher:
  Indiana Department of Transportation
• Description:
  The following five methods of regionalization of watersheds were tested with Indiana watershed and annual maximum flood data: (1) the L-moment based method, (2) the method based on hybrid cluster analysis, (3) the hybrid cluster method using rainfall data, (4) the fuzzy cluster method, and (5) the method based on artificial neural networks. The results of the L-moment based method and the hybrid cluster method with rainfall data were unacceptable because of the subjectivity involved with the former and the heterogeneity of the of the results obtained by the latter. The remaining three methods gave very similar results. The fuzzy cluster and artificial neural network-based methods are much easier to use and hence are recommended. The results from any of these methods will not give homogeneous regions. The results from the clustering methods must be tested and revised to get homogeneous watersheds. The data from each of the regions were investigated by using tests based on simple scaling. The results from these tests confirm all the regions, except one, to be homogeneous.
• Format:
  PDF
• Funding:
  SPR-2476
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/90220/dot_90220_DS1.pdf
• File Type:
  [PDF - 1.07 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:735cf827454f989125c8dab081111df4716f3a8ddcfa2a4bdaa24e241e7869d341917e88a02de11d1c82b34dcd921604fe4d4b3af051cd49a5ec5d2de09387af