Flooding is one of the most catastrophic natural disasters in terms of provoking socioeconomic losses.The current study is to foster a ood 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 ood 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 scienti c 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 ood hazard modeling and ascertaining the Flood Hazard Index (FHI) to produce a ood 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 ood. According to the ndings, the Flood Hazard Index is 42% and the study region is classi ed into high, moderate, and low susceptible respectively. on. Furthermore, the lack of high-temporal-scale remote sensing data is a key impediment to ood mapping.The AHP model's ood vulnerability zone was classi ed into three groups (high, moderate, and low). The ndings clearly show that regions with low elevation and slope, high TWI, low SPI, high drainage density, and high precipitation are primarily risk-prone due to the high potential of ooding in such areas. The AHP technique used expert judgment and expertise to determine the best weights for the components that contribute to ood risk. The ease of handling, exibility, and low cost of the applicable technique make it possible to employ, particularly in such a location with relatively limited data and information. In the present preliminary assessment of ood hazards, the AHP technique serves its purpose.As, food risk management is a crucial segment of all social and environmental processes aimed at minimizing loss of life, injury, and property damage. The ndings of this research will assist decisionmakers in carrying out adequate ood management in future. Finally, the present study's maps may be utilized as a reference for ood prevention and preparedness by planners, developers, and the government. Knowledge of high-risk zones is essential for local governments to adequately control ooding and plan the execution of necessary ood prevention systems.

Groundwater supplies across the world are under tremendous strain due to overuse and noticeable climatic changes over time. The requirement to assess groundwater potential and aquifer productivity rises along with the global need for potable water for human consumption, agriculture, and industrial applications. Because they are quick and will give first-hand knowledge on the resource for future projects, geographic information system-based studies have recently become quite popular in groundwater exploration. With this in mind, the current work uses remote sensing and GIS techniques to select and define groundwater potential zones for the evaluation of groundwater availability in the Srikakulam district of Andhra Pradesh, India.In the current work, an analytical hierarchical process approach (AHP) was combined with a geographic information system. For the purpose of defining the groundwater potential zone, a total of 12 thematic layers, including slope, rainfall, curvature, soil, drainage density, lineament density, topographic wetness index, land surface temperature, elevation, land use & land cover, lithology, and groundwater fluctuation, were taken into consideration. According to their qualities and water potential capacity as determined by the AHP technique, weights are allocated to each class in all thematic maps. To determine the groundwater potential zones, overlay analysis was performed after the creation of all the maps. The resulting groundwater potential zone map, which had a ground water potential index of 33, was divided into five classes which are ranging from very high to very low.

Among nature's most insidious hazards is drought. The consequences vary from region to region, and it is sometimes called a 'creeping phenomenon'. Our societies are increasingly being affected by droughts that have developed slowly and steadily over several years. Farming is negatively affected by droughts, which can result in devastating losses. The severity and frequency of droughts can vary widely depending on several factors, including climatic conditions, temperature, and economic conditions, such as population density and irrigated land conditions. Therefore, in comparison to conventional methods, remote sensing-based studies provide the most comprehensive monitoring and mapping of droughts. The study illustrates how Geographic Information Systems (GIS) can aid drought vulnerability assessments through the use of analytical hierarchy analysis and geographical information systems. This study uses GIS for spatial analysis of drought in Kurnool district, Andhra Pradesh, India. Thirteen parameters such as Slope, Elevation, Aspect, Soil texture, Geology/Lithology, Land use & Land cover (LULC), Drainage density, Distance from the water bodies, Ground water fluctuation, Normalized difference vegetation index (NDVI), Rainfall, Land Surface Temperature (LST) and Topographic wetness index (TWI)were chosen and considered for the study. In order to produce drought maps of both spatial and temporal extent, these indices were integrated. Using pair-wise comparison matrices, AHP calculates weights for each criterion. Drought Vulnerability Assessment (DVA) map is generated by analyzing the thematic maps of all the parameters. According to the combined multi-criteria decision making and GIS results, the drought vulnerability index is 42.5. Using the output DVA map, ample information will be available on drought severity in the region and agricultural vulnerability. Accordingly, this study proposes combining AHP with GIS to map drought regions.

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.