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<p>This study applies an integrated GIS and Exploratory Regression Modeling (ERM) approach to assess flood susceptibility in the Muhuri River Basin, South Tripura, India, a region characterized by undulating terrain and intense monsoonal rainfall leading to recurrent flooding. Twelve flood conditioning factors—elevation, slope, topographic wetness index (TWI), topographic position index (TPI), stream power index (SPI), distance from river (DFR), drainage density (DD), population density, land use land cover (LULC), rainfall, soil type, and normalized difference vegetation index (NDVI) were analyzed to evaluate their relative influence on flood susceptibility. The results indicate that drainage density, population density, elevation, and distance from river are the most influential factors, while the optimal model integrating drainage density, soil type, distance from river, population density, and rainfall achieved a high explanatory power (adjusted R² = 0.81) with the lowest Akaike’s Information Criterion (AIC = 897.73). The generated flood susceptibility map shows that 35.03% of the basin falls under high to very high susceptibility zones, primarily located in the central and southeastern regions. Model validation using Global Moran’s I (I = 0.09, Z = 1.41, <i>p</i> = 0.15) confirms the lack of spatial autocorrelation in residuals, indicating model robustness. The findings demonstrate the effectiveness of ERM as a transparent and interpretable tool for flood susceptibility assessment and provide valuable insights for targeted flood mitigation, early warning systems, and land use planning in the Muhuri River Basin and similar flood-prone regions.</p>

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GIS and exploratory regression-based flood susceptibility mapping in the Muhuri River Basin, India

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Asif Iqbal Shah,
  • Jimi Basumatary,
  • Nibedita Das Pan

摘要

This study applies an integrated GIS and Exploratory Regression Modeling (ERM) approach to assess flood susceptibility in the Muhuri River Basin, South Tripura, India, a region characterized by undulating terrain and intense monsoonal rainfall leading to recurrent flooding. Twelve flood conditioning factors—elevation, slope, topographic wetness index (TWI), topographic position index (TPI), stream power index (SPI), distance from river (DFR), drainage density (DD), population density, land use land cover (LULC), rainfall, soil type, and normalized difference vegetation index (NDVI) were analyzed to evaluate their relative influence on flood susceptibility. The results indicate that drainage density, population density, elevation, and distance from river are the most influential factors, while the optimal model integrating drainage density, soil type, distance from river, population density, and rainfall achieved a high explanatory power (adjusted R² = 0.81) with the lowest Akaike’s Information Criterion (AIC = 897.73). The generated flood susceptibility map shows that 35.03% of the basin falls under high to very high susceptibility zones, primarily located in the central and southeastern regions. Model validation using Global Moran’s I (I = 0.09, Z = 1.41, p = 0.15) confirms the lack of spatial autocorrelation in residuals, indicating model robustness. The findings demonstrate the effectiveness of ERM as a transparent and interpretable tool for flood susceptibility assessment and provide valuable insights for targeted flood mitigation, early warning systems, and land use planning in the Muhuri River Basin and similar flood-prone regions.