Integrated approach for spatial flood susceptibility assessment in Bhagirathi sub‐basin, India using entropy information theory and geospatial technology

Abstract

Globally, floods as dynamic hydraulic hazard have caused widespread damages to both socioeconomic conditions and environment at various scales. Managing flood and management of water resource is a global challenge under the changing climatic condition. This study assessed flood susceptibility in the Bhagirathi sub-basin, India using entropy information theory and geospatial technology. Twelve flood susceptibility parameters such as land use/land cover, normalized difference vegetation index (NDVI), slope, elevation, geology, geomorphology, normalized difference water index (NDWI), soil, drainage density, average rainfall, maximum temperature, and humidity during monsoon season were utilized to examine flood susceptibility. Receiver operating characteristics (ROC) curve and Leave-One-Out Cross-Validation (LOOCV) techniques were carried out to validate flood susceptibility map. Kappa statistics was also used to check the reliability of the flood susceptibility model. Findings of the study revealed that nearly 45% area of the sub-basin was highly susceptible to flood followed by moderate (29.3%), very high (19%), low (6.9%), and very low (0.2%). These findings also revealed that nearly 92% area in the eastern, north-eastern, and deltaic sub-basin was susceptible to floods. ROC analysis indicated high success (0.932) and prediction (0.903) rates for the susceptibility map while LOOCV (R 2 being 0.97) and Kappa (k = 0.934) have shown substantial prediction of the model. Hence, the susceptibility maps are useful for the local planners and government organization in designing the early flood warning system, and reducing the human and economic losses. The methodology used in this study is applicable for analyzing flood susceptibility at spatial scales in similar systems.

Deciphering Urbanization and Spatial Disparity in South 24 Parganas district of West Bengal, India

“…We utilized cloud-free Landsat 4-5, 7 and 8 photos from 29 October 1990 to 21 December 2021 (https://glovis.usgs.gov) to examine urban growth in South 24 Parganas. Deciphering Urbanization and Spatial Disparity in South 24 Parganas District of West Bengal, India Rehman et al, 2022). Classification accuracy is often verified by comparing reference data with remote sensing data analysis (Congalton, 1991;Alam et al, 2022).…”

Assessment of Inundation Susceptibility in the Context of Climate Change, Based on Machine Learning and Remote Sensing: Case Study in Vinh Phuc Province of Vietnam

“…The AUC (area under the curve) ROC summarizes the overall performance of a classification model, with a value ranging from 0 to 1. A value of 1 indicates a perfect model, while 0.5 represents a non-informative model (Nachappa et al 2020, Rehman et al 2022.…”

Appraisal of Flood Susceptibility of Hooghly basin, India using Shannon Entropy (SE) and Fuzzy Analytical Hierarchy Process (FAHP)

“…Normalized difference water index (NDWI) was used to distinguish the saturated water surfaces in the basin (Dash and Sar 2020;Rehman et al 2022). NDWI (Fig.…”

“…The river basins are globally susceptible to flooding at varying levels due to increases in temperature (He et al 2022). Studies are focusing on flood hazard susceptibility assessment in river basins using various approaches (Patrikaki et al 2018;Maharana et al 2022;Rehman et al 2022). Fox Gotham et al (2017) examined post disaster vulnerability and risk perception using logistic regression in New Orleans.…”

“…Out of the total geographical area of India (329 million hectares), around 40 mha are prone to floods. Rising precipitation over the central of India is often caused by the changes in the Indian equatorial sea surface temperature (Rehman et al 2022). Major flood-prone areas in India are the Indo-Gangetic plains, Brahmaputra basins, and plains of northeast India which contribute to nearly 60% of total river flow (Gandhi and Johnson 2019).…”