Application of Geographical Information System-Based Analytical Hierarchy Process Modeling for Flood Susceptibility Mapping of Krishna District in Andhra Pradesh

Abstract

Flooding is one of the most catastrophic natural disasters in terms of provoking socioeconomic losses. The current study is to foster a flood susceptibility map of Krishna District in Andhra Pradesh (AP) through integrating remote sensing data, geographical information system (GIS), and the Analytical hierarchy process (AHP). Eleven factors including elevation, slope, aspect, land-use/land-cover (LULC), drainage density, topographic wetness index, stream power index, lithology, soil, precipitation, distance from the streams, are considered for identifying and evaluating the spatial distribution of critical flood susceptible regions. Thematic maps of different factors were derived in GIS using remote sensing data obtained from Sentinel-2A (satellite sensor), shuttle radar topography mission digital elevation model (SRTM DEM – v3), and other scientific data products. An analytical hierarchy process is a mathematical approach for decision support, primarily based on the weight and rank of different causative factors. AHP technique is implemented for flood hazard modeling and ascertaining the Flood Hazard Index (FHI) to produce a flood susceptibility map. Different thematic maps weighed with the AHP framework are combined using overlay analysis to produce the Flood Susceptibility map of the study region. The outcomes of the study demonstrate the potential of GIS and AHP in providing a premise to recognize the vulnerable areas that are susceptible to flood. According to the findings, the Flood Hazard Index is 42% and the study region is classified into high, moderate, and low susceptible respectively.

Crop-land suitability analysis using geographic information system and remote sensing

“…The soil texture is important because it determines the soil properties that impact plant development (Hassan et al, 2020;Rao et al, 2018;Ravinder and Ramu, 2020). Three of these attributes are affected by soil texture: water holding capacity, permeability, and soil workability (Everest et al, 2020;Penki et al, 2022a;Romeijn et al, 2016). Water retention capacity refers to a soil's ability to hold water (Ravinder and Ramu, 2020).…”

“…If the soil is too wet, nutrients may wash off and not reach the plant's roots, as a result, growth and general health suffer (Everest et al, 2020). In addition, too much rain can encourage the growth of bacteria, fungi, and mold in the soil (Penki et al, 2022a). For this study area, we used rainfall data from the APSDPS website (Andhra Pradesh State Development Planning Society) for Vizianagaram district, Mandal level data is taken.…”

“…For doing the crop-land suitability analysis using geographical information system we employed a method called Analytical Hierarchy Process. One example of a multiple-criteria decision-making (MCDM) procedure is the Analytic Hierarchy Process (AHP) (Penki et al, 2022a(Penki et al, , 2022bRamu et al, 2020;Ravinder and Ramu, 2020;Saaty, 1990;Saha et al, 2021). Similar other MCDM methods are Electre, Topsis, Promethee, Grey theory.…”

“…Similar other MCDM methods are Electre, Topsis, Promethee, Grey theory. AHP offers many choices based on the relative relevance and weight of the factors inside a hierarchical structure (Penki et al, 2022a;Ramu et al, 2020;Rao et al, 2018;Saaty, 1990). AHP is a system that employs a multi-criteria hierarchical approach (Penki et al, 2022a).…”

“…AHP offers many choices based on the relative relevance and weight of the factors inside a hierarchical structure (Penki et al, 2022a;Ramu et al, 2020;Rao et al, 2018;Saaty, 1990). AHP is a system that employs a multi-criteria hierarchical approach (Penki et al, 2022a). To establish the relative relevance of a parameter to other factors on a level, the method uses scoring and a pairwise comparison matrix (Hassan et al, 2020).…”