BUILDING A PROBABILITY FLOOD RISK MODEL, USING GIS, LOGISTIC REGRESSION AND FUZZY WEIGHTS

被引:0
|
作者
Kotinas, V. [1 ]
机构
[1] Univ Athens, Dept Geog & Climatol, Fac Geol & Geoenvironm, Athens, Greece
关键词
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
As flash flood events are becoming more frequent, the development of flood risk estimation methods is becoming more important than ever before. The aim of this paper is to develop a methodology in order to study the flood risk in a river catchment by taking into account various parameters that are organized in a spatial database (geological, geomorphological, topographic and land use data). The collected data are analyzed through the use of G.I.S software (ArcMap) in order to generate the appropriate steps for the formation of the flood risk model. Finally, we proceed to develop a probabilistic flood risk model (based on the logistic regression) using as variables the lithology, the slope, land use, average basin altitude. All the above variables are multiplied by the proper weightsin the form of triangular fuzzy numbers. These weights are related to the importance of each involved variable. This model has been applied in the area of Samos Island, Greece with success. The proposed methodology and the preliminary results, as exported for the entire island prove the suitability of this method in the creation of flood risk maps.
引用
收藏
页码:759 / 764
页数:6
相关论文
共 50 条
  • [1] Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors
    Ahmed E. M. Al-Juaidi
    Ayman M. Nassar
    Omar E. M. Al-Juaidi
    Arabian Journal of Geosciences, 2018, 11
  • [2] Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors
    Al-Juaidi, Ahmed E. M.
    Nassar, Ayman M.
    Al-Juaidi, Omar E. M.
    ARABIAN JOURNAL OF GEOSCIENCES, 2018, 11 (24)
  • [3] EMPIRICAL MODELLING OF WINDTHROW RISK USING GIS AND LOGISTIC REGRESSION
    Krejci, L.
    GEOGRAPHIA TECHNICA, 2010, 5 (01): : 25 - 35
  • [4] Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain)
    Lamelas, M. T.
    Marinoni, O.
    Hoppe, A.
    de la Riva, J.
    ENVIRONMENTAL GEOLOGY, 2008, 54 (05): : 963 - 977
  • [5] Deforestation modelling using logistic regression and GIS
    Faculty of Natural Resources, Center for Research and Development of Northern Zagros Forests, University of Kurdistan, Sanandaj, Iran
    J. For. Sci., 5 (193-199):
  • [6] Flash Flood Forecasting in Sao Paulo Using a Binary Logistic Regression Model
    Viteri Lopez, Andrea Salome
    Morales Rodriguez, Carlos Augusto
    ATMOSPHERE, 2020, 11 (05)
  • [7] Demand analysis of flood insurance by using logistic regression model and genetic algorithm
    Sidi, P.
    Mamat, M. B.
    Sukono
    Supian, S.
    Putra, A. S.
    INDONESIAN OPERATIONS RESEARCH ASSOCIATION - INTERNATIONAL CONFERENCE ON OPERATIONS RESEARCH 2017, 2018, 332
  • [8] Linking Building Attributes and Tornado Vulnerability Using a Logistic Regression Model
    Egnew, Alyssa C.
    Roueche, David B.
    Prevatt, David O.
    NATURAL HAZARDS REVIEW, 2018, 19 (04)
  • [9] Intuitionistic Fuzzy Partial Logistic Regression Model Using Ridge Methodology
    Hesamian, Gholamreza
    Akbari, Mohammad Ghasem
    Roozbeh, Mehdi
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2020, 28 (04) : 527 - 543
  • [10] Logistic regression model for evaluating soil liquefaction probability using CPT data
    Lai, Sheng-Yao
    Chang, Wen-Jong
    Lin, Ping-Sien
    JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING, 2006, 132 (06) : 694 - 704