Abstract
Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths.
Highlights
Flood is among the most severe natural disasters; it causes significant and irreversible damage to property and communication infrastructure, which leads to considerable loss of life, both human and livestock, along with loss of agricultural produce and farm lands
The adaptive neuro fuzzy interface system used for landslide susceptibility in Qazvin Province, Iran [12], hydraulic modeling used for estimating unsaturated soil hydraulic conductivity [13], and soil water assessment tool (SWAT) in the ArcGIS software environment [14] are some models utilized for flood susceptibility estimation
These primary criteria were classified under five major groups based on similar properties and coherence, namely (I):hydrological criterion: precipitation, river network density, and stream power index (SPI); (II): morphometric criterion: elevation, slope, profile curvature, landforms, ruggedness index, and distance from rivers; (III): permeability criterion: soil type, soil moisture, Topographic wetness index (TWI), soil erodibility factor (K), and rainfall erosivity factor (R); (IV): land use and land cover management (LU/LC) dynamics criterion: LU/LC, soil-adjusted vegetation index (SAVI), and normalized differential vegetation index (NDVI); (V): anthropogenic interference: population density, global man-made impervious surface (GMIS), global human built-up and settlement extent (HBASE), and distance from roads
Summary
Flood is among the most severe natural disasters; it causes significant and irreversible damage to property and communication infrastructure, which leads to considerable loss of life, both human and livestock, along with loss of agricultural produce and farm lands. Some of the deadliest floods occurred in China (1935, 1931, 1887), Guatemala (1949), Bangladesh (1974), Venezuela (1999), Iran (1954), India (2013), Japan (1953), and Peru (1941), and many more, where loss of human life occurred in the range of several thousands These natural disasters can be monitored properly using modern technology and information systems. The adaptive neuro fuzzy interface system used for landslide susceptibility in Qazvin Province, Iran [12], hydraulic modeling used for estimating unsaturated soil hydraulic conductivity [13], and soil water assessment tool (SWAT) in the ArcGIS software environment [14] are some models utilized for flood susceptibility estimation Deep learning methods, such as artificial neural networks (ANNs), fuzzy logic, support vector machines, random forest classification, regression trees (RTs), and classification and RT (CART) algorithms [15,16,17], have significant potential for effective flood mapping and monitoring. With several rivers flowing through, Bihar is a flood-prone region; it constitutes 16.5% of the total flood area and is home to 22.1% of the flood-affected population of India [26]
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