IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v61y2012i3p1427-1442.html
   My bibliography  Save this article

Identification of homogenous regions in Gorganrood basin (Iran) for the purpose of regionalization

Author

Listed:
  • Atefeh Abdolhay
  • Bahram Saghafian
  • Mohd Soom
  • Abdul Ghazali

Abstract

Estimation of flood in basins with poor condition of hydrometric stations as in quantity and quality is a dominant problem around the world, mainly in developing country where lack of funds and human resources cause more limitation in number of gauging stations. One of the areas that experience frequent floods and also suffer from small number of stations in Iran is Gorganrood basin. So there is a great need for the estimation and prediction of runoff in this area to prevent any future floods. Due to insufficient station in this area, direct prediction of flood is not applicable. Regional flood frequency analysis is a practical and widely used solution for these situations, which involves the identification of homogenous regions. Gorganrood region was hydrologically homogenized according to the extracted parameters that influence the floods. One of these parameters was Normalized Difference Vegetation Index (NDVI) driven from MODIS images. Curvature is another parameter that relates to topographic attributes. From factor analysis, the most appropriate variables were selected. According to these parameters (NDVI, curvature, area, slope…), the regions were classified into homogenous regions. For the purpose of homogenization, hierarchical (wards) clustering, fuzzy clustering and Kohonen method were applied. L-moment technique was used for the investigation of the results. The heterogeneity measure for one of the groups (Group 1) was more than two; therefore some modifications were applied. The region was grouped into two homogenous subregions. All of the clustering methods showed same results. The models showed that class 4 of NDVI is influential on flood in some return periods. The resulted models can be applied in future studies in different aspects of practical hydrology. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Atefeh Abdolhay & Bahram Saghafian & Mohd Soom & Abdul Ghazali, 2012. "Identification of homogenous regions in Gorganrood basin (Iran) for the purpose of regionalization," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(3), pages 1427-1442, April.
  • Handle: RePEc:spr:nathaz:v:61:y:2012:i:3:p:1427-1442
    DOI: 10.1007/s11069-011-0076-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-011-0076-2
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11069-011-0076-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zaw Latt & Hartmut Wittenberg & Brigitte Urban, 2015. "Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments: an Example of the Chindwin River in Myanmar," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(3), pages 913-928, February.
    2. Ali Ahani & S. Saeid Mousavi Nadoushani & Ali Moridi, 2020. "Regionalization of watersheds based on the concept of rough set," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 883-899, October.
    3. Reza Zamani & Hossein Tabari & Patrick Willems, 2015. "Extreme streamflow drought in the Karkheh river basin (Iran): probabilistic and regional analyses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 327-346, March.
    4. Omid Rahmati & Ali Haghizadeh & Stefanos Stefanidis, 2016. "Assessing the Accuracy of GIS-Based Analytical Hierarchy Process for Watershed Prioritization; Gorganrood River Basin, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1131-1150, February.
    5. Omid Rahmati & Hamid Reza Pourghasemi, 2017. "Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1473-1487, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manuel Chaves-Maza & Eugenio M. Fedriani Martel, 2020. "Entrepreneurship support ways after the COVID-19 crisis," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 662-681, December.
    2. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
    3. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 39-54, January.
    4. repec:onb:oenbwp:y:2005:i:9:b:1 is not listed on IDEAS
    5. Guobin Fu & Stephanie R. Clark & Dennis Gonzalez & Rodrigo Rojas & Sreekanth Janardhanan, 2023. "Spatial and Temporal Patterns of Groundwater Levels: A Case Study of Alluvial Aquifers in the Murray–Darling Basin, Australia," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    6. Pérez-Campuzano, Darío & Rubio Andrada, Luis & Morcillo Ortega, Patricio & López-Lázaro, Antonio, 2022. "Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance," Journal of Air Transport Management, Elsevier, vol. 101(C).
    7. Paradi, Joseph C. & Zhu, Haiyan & Edelstein, Barak, 2012. "Identifying managerial groups in a large Canadian bank branch network with a DEA approach," European Journal of Operational Research, Elsevier, vol. 219(1), pages 178-187.
    8. Lozano, S. & Guerrero, F. & Onieva, L. & Larraneta, J., 1998. "Kohonen maps for solving a class of location-allocation problems," European Journal of Operational Research, Elsevier, vol. 108(1), pages 106-117, July.
    9. Onsel Sahin, Sule & Ulengin, Fusun & Ulengin, Burc, 2004. "Using neural networks and cognitive mapping in scenario analysis: The case of Turkey's inflation dynamics," European Journal of Operational Research, Elsevier, vol. 158(1), pages 124-145, October.
    10. Antonio Russo & Ian Smith, 2012. "Attractive regions: for whom? And how does that matter?," ERSA conference papers ersa12p362, European Regional Science Association.
    11. Pankaj Kumar Medhi & Sandeep Mondal, 2016. "A neural feature extraction model for classification of firms and prediction of outsourcing success: advantage of using relational sources of information for new suppliers," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6071-6081, October.
    12. Ramin Baghai‐Wadji & Rami El‐Berry & Stefan Klocker & Markus Schwaiger, 2006. "Changing investment styles: style creep and style gaming in the hedge fund industry," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(4), pages 157-177, October.
    13. Ozer, Muammer, 2005. "Fuzzy c-means clustering and Internet portals: A case study," European Journal of Operational Research, Elsevier, vol. 164(3), pages 696-714, August.
    14. Ulengin, Fusun & Ulengin, Burc & Onsel, Sule, 2002. "A power-based measurement approach to specify macroeconomic competitiveness of countries," Socio-Economic Planning Sciences, Elsevier, vol. 36(3), pages 203-226, September.
    15. Marcus Deetz, 2019. "K-Means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of Self-Classification of Hedge Fund Managers," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 5(3), pages 43-57, March.
    16. Z Hua & S Li & Z Tao, 2006. "A rule-based risk decision-making approach and its application in China's customs inspection decision," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1313-1322, November.
    17. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    18. Marzieh MOKARRAM & Mahdi NAJAFI-GHIRI & Abdol Rassoul ZAREI, 2018. "Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 13(1), pages 11-17.
    19. Eun Sun Kim & Yunjeong Choi & Jeongeun Byun, 2019. "Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    20. Curry, B. & Morgan, P. H., 2004. "Evaluating Kohonen's learning rule: An approach through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 154(1), pages 191-205, April.
    21. Machado de CAMPOS, Silvia Regina & Henriques, Roberto & Yanaze, Mitsuru Higuchi, 2019. "Knowledge discovery through higher education census data," Technological Forecasting and Social Change, Elsevier, vol. 149(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:61:y:2012:i:3:p:1427-1442. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.