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Assessment of urban flood susceptibility and role of urban green space (UGS) on flooding susceptibility using GIS-based probabilistic models

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Abstract

With rapid urbanization, the green space in urban areas is replaced with impervious built-up areas, which increases the frequency of urban floods. Kamrup Metropolitan District, Assam, is near the Brahmaputra and is highly prone to urban flooding. The present study aims to develop the urban flood susceptibility index (FSI) and to analyze the role of urban green space (UGS) as a nature-based solution (NBS) for urban flood susceptibility. Two types of flooded urban areas are observed using a two-stage cluster analysis. A GIS-based urban FSI is developed using logistic regression (LR), frequency ratio (FR), Shannon entropy (SE), certainty factor (CF), and weight of evidence (WoE) models, and variation of FSI is assessed for different UGS areas. According to the area under curve (AUC), the performance of all five models falls under the good to excellent class. The average UGS ratio for non-flooded is higher than for flooded areas, and with an increase in the area of UGS, the flooding probability decreases for all the models. The findings of the present study emphasize the importance of UGS and can be used for effective urban flood risk mitigation and management planning.

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The data supporting the findings of this study are available within the article, and derived data supporting the results of this study are available upon reasonable request.

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Conceptualization: Jagabandhu Dixit; methodology: Laxmi Gupta, Jagabandhu Dixit; formal analysis and investigation: Laxmi Gupta; data curation and software: Laxmi Gupta; validation: Laxmi Gupta; visualization: Laxmi Gupta, Jagabandhu Dixit; writing—original draft: Laxmi Gupta; writing—review and editing: Jagabandhu Dixit; resources: Laxmi Gupta, Jagabandhu Dixit; supervision: Jagabandhu Dixit.

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Gupta, L., Dixit, J. Assessment of urban flood susceptibility and role of urban green space (UGS) on flooding susceptibility using GIS-based probabilistic models. Environ Monit Assess 195, 1518 (2023). https://doi.org/10.1007/s10661-023-12061-4

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