Application of the GIS-Based Probabilistic Models for Mapping the Flood Susceptibility in Bansloi Sub-basin of Ganga-Bhagirathi River and Their Comparison

Abstract

Flood is one of the devastating environmental hazards, causes massive economic breakdown and enormous loss of life, and destructs the houses in the affected areas. The present work intends to prepare the spatial flood susceptibility maps (FSMs) applying the probabilistic models, viz, frequency ratio (FR), weights of evidence (WoE), Shannon’s entropy (SE), conditional probability (CP), and certainty factor (CF) and find out the most effective model among them. A supervised neural network technique, i.e., learning vector quantization (LVQ) algorithm, has been employed to trace the involvement of conditioning factors for flood occurrences. For this work, twelve morphological, hydrological, and lithological flood conditioning factors, i.e., elevation, slope steepness, drainage density, distance from the river, plan curvature, topographical wetness index, stream power index, rainfall, normalized difference vegetation index, normalized difference water index, lithology, and land use/land cover, have been employed in the geographic information system (GIS) environment. Weights of each class of each factor have been computed through the four models except Shannon’s entropy, as it defines only factor weights. Finally, FSMs of each model are showing 9.61% (FR), 11.69% (WoE), 9.47% (SE), 11.17% (CF), and 10.99% (CP) areas of the basin under the most susceptible zone, respectively. The performances of the models have been evaluated using receiver operating characteristics (ROC) and seven statistical diagnostic tests. The results confirm that the CF and CP models are more adaptable than others. This flood-susceptible map can be used for flood management planning to prevent and lessen anticipated losses.