IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i13d10.1007_s11269-024-03918-5.html
   My bibliography  Save this article

Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees

Author

Listed:
  • Hüseyin Akay

    (Gazi University)

Abstract

Accurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (Cc) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and Cc-Euclidean, 0.996 for Cc-Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, Cc-Euclidean, and Cc-Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.

Suggested Citation

  • Hüseyin Akay, 2024. "Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5365-5383, October.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03918-5
    DOI: 10.1007/s11269-024-03918-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03918-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03918-5?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. Hüseyin Akay, 2022. "Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6205-6222, December.
    2. Huchang Liao & Zeshui Xu & Meimei Xia, 2014. "Multiplicative Consistency Of Hesitant Fuzzy Preference Relation And Its Application In Group Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 47-76.
    3. Manish Singh Rana & Chandan Mahanta, 2023. "Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region," 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. 115(1), pages 947-969, January.
    4. Yangfan Xiao & Shanzhen Yi & Zhongqian Tang, 2018. "A Spatially Explicit Multi-Criteria Analysis Method on Solving Spatial Heterogeneity Problems for Flood Hazard Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3317-3335, August.
    5. Karim Solaimani & Fatemeh Shokrian & Shadman Darvishi, 2023. "An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 403-425, January.
    Full references (including those not matched with items on IDEAS)

    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. Mohammed Ifkirne & Houssam El Bouhi & Siham Acharki & Quoc Bao Pham & Abdelouahed Farah & Nguyen Thi Thuy Linh, 2022. "Multi-Criteria GIS-Based Analysis for Mapping Suitable Sites for Onshore Wind Farms in Southeast France," Land, MDPI, vol. 11(10), pages 1-26, October.
    2. Karim Solaimani & Fatemeh Shokrian & Shadman Darvishi, 2023. "An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 403-425, January.
    3. Qasim Noor & Tabasam Rashid & Syed Muhammad Husnine, 2021. "An extended TDM method under probabilistic interval-valued hesitant fuzzy environment for stock selection," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.
    4. Mousavi, M. & Gitinavard, H. & Mousavi, S.M., 2017. "A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 774-787.
    5. Wanying Xie & Zeshui Xu & Zhiliang Ren & Hai Wang, 2018. "Probabilistic Linguistic Analytic Hierarchy Process and Its Application on the Performance Assessment of Xiongan New Area," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1693-1724, November.
    6. Lihong Wang & Zaiwu Gong, 2017. "Priority of a Hesitant Fuzzy Linguistic Preference Relation with a Normal Distribution in Meteorological Disaster Risk Assessment," IJERPH, MDPI, vol. 14(10), pages 1-16, October.
    7. Erfan Mahmoodi & Mahmood Azari & Mohammad Taghi Dastorani & Aryan Salvati, 2024. "Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5343-5363, October.
    8. Ming Zhong & Jiao Wang & Liang Gao & Kairong Lin & Yang Hong, 2019. "Fuzzy Risk Assessment of Flash Floods Using a Cloud-Based Information Diffusion Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2537-2553, May.
    9. Akshay Hinduja & Manju Pandey, 2019. "A Distance-based Method for Computing Priorities of Intuitionistic Fuzzy Preference Relation and Its Application in AHP," Vision, , vol. 23(4), pages 329-340, December.
    10. Xiaolin Xun & Yongbo Yuan, 2020. "Research on the urban resilience evaluation with hybrid multiple attribute TOPSIS method: an example in China," 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. 103(1), pages 557-577, August.
    11. Hüseyin Akay, 2022. "Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6205-6222, December.
    12. Xiaoyan Zhang & Qian Yang, 2022. "A Novel Method for Decision Making by Double-Quantitative Rough Sets in Hesitant Fuzzy Systems," Mathematics, MDPI, vol. 10(12), pages 1-24, June.

    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:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03918-5. 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.