Appraisal of flood susceptibility of Hooghly basin, India using Shannon entropy (SE) and fuzzy analytical hierarchy process (FAHP)

被引:0
|
作者
Rehman, Sufia [1 ]
Chaudhary, Bhagwan Singh [2 ]
Azhoni, Adani [1 ]
机构
[1] Natl Inst Technol Manipur, Dept Civil Engn, Imphal, Manipur, India
[2] Kurukshetra Univ, Dept Geophys, Thanesar, Haryana, India
关键词
Hooghly basin; Flood susceptibility; MCDM; FAHP; SE; MULTICRITERIA DECISION-MAKING; NINO SOUTHERN-OSCILLATION; MACHINE LEARNING-MODELS; RISK; RIVER; BIVARIATE;
D O I
10.1007/s12665-024-11751-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Flooding is the most frequent phenomenon that leads to social and economic disruption worldwide. Effective flood management necessitates an understanding of the spatial distribution of flood-susceptible areas. Currently, the delineation of flood-susceptible areas and their effective management are a significant theme of flood research. However, in order to ensure sustainable flood management, the precise identification of flood-susceptible areas is yet to be explored. The Hooghly basin is one of the flood-affected areas of West Bengal in India, where the dense river system, topography, and geographic location make it more susceptible to flooding. The major aim of this study is to examine the flood susceptibility of the Hooghly basin using two significant methods of multicriteria decision making models (MCDM) i.e., Shannon entropy (SE) and fuzzy analytical hierarchy process (FAHP) models. Multicriteria decision-making models have been found to be effective in evaluating factors for decision making. These two models were applied to twelve flood conditioning factors such as drainage density, elevation, LULC, normalized difference built up index (NDBI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), population density, rainfall, distance to rivers, slope, stream power index (SPI) and topographic wetness index (TWI). Binary logistic regression was also performed to identify the confounders of susceptibility in the basin. Findings indicated that nearly 56-58% area of the basin susceptible to floods. Drainage density, elevation, rainfall, and distance to rivers were identified as the major determinants of flood susceptibility. Validation of the models through the area under ROC curve (AUC) showed good predictability for SE (0.70) and FAHP (0.71) based flood susceptibility prediction. Findings of this study may provide a base for stakeholders and planners in managing and minimizing flood susceptibility in the basin.
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页数:20
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