Assessment of Groundwater Potential in the Kalahandi District of Odisha (India) Using Remote Sensing, Geographic Information System and Analytical Hierarchy Process

2020 ◽  
Vol 48 (12) ◽  
pp. 1739-1753
Author(s):  
T. Goitsemang ◽  
D. M. Das ◽  
S. K. Raul ◽  
C. R. Subudhi ◽  
B. Panigrahi
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Analytical Hierarchy Process ◽  
Groundwater Potential ◽  
Geographic Information ◽  
Hierarchy Process

scholarly journals Application of RS/GIS in Conservation Studies for Surface and Groundwater Harvesting in Cold Arid Regions of Northwestern Himalayas

2020 ◽  
Vol 36 (1) ◽  
pp. 105-114
Author(s):  
Junaid N. Khan ◽  
Syed Rouhullah Ali ◽  
Asima Jillani ◽  
Ifra Ashraf
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Variability ◽  
Analytical Hierarchy Process ◽  
Temporal Variability ◽  
Arid Regions ◽  
Geographic Information ◽  
Water Harvesting ◽  
Hierarchy Process

Abstract.The availability of erratic rainfall and high evapotranspiration causes temporal and spatial variability of water thereby causing crop yield reduction and crop failure. The potential of water harvesting (WH) both groundwater as well as surface water to mitigate the spatial and temporal variability of precipitation. One technique for water harvesting (WH) is to collect excess runoff water both rain and snowmelt, store it for agricultural purposes during dry spells. The present work accentuated the expediency of remote sensing (RS) and geographic information system (GIS) applications in water harvesting studies. The resultant water harvesting potential map prepared was thus classified into three WH potential zones namely, high, medium and low covering an area of 32.82, 10320.10, and 7596.18 ha (<1%, 57.49%, and 42.32%) respectively. The groundwater map in the area was also classified as high potential areas covering 1421.69 ha (7.92%), medium potential areas covering 8762.69 ha (48.81%), and low potential areas covering 7764.72 ha (43.25%). The integrated remote sensing (RS), Geographical Information System (GIS), Soil and Water Assessment Tool (SWAT), and analytical hierarchy process (AHP) were found to be efficient methods to recover water and to select suitable water and groundwater harvesting sites in order to ensure better water accessibility to the people for domestic, irrigation and other activities in cold arid regions of northwestern Himalayas. Keywords: Analytical hierarchy process, Geographic Information System, Groundwater harvesting, Remote sensing, Spatial variability, Temporal variability, Water harvesting.

Mapping of groundwater potential zones using remote sensing and geographic information system: A case study of parts of Tigray, Ethiopia

2018 ◽  
Vol 25 (4) ◽  
pp. 133-140 ◽  
Author(s):  
Tesfa Gebre ◽  
Imran Ahmad ◽  
Mithas Ahmad Dar ◽  
Edissa Gadissa ◽  
Afera Halefom Teka ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Groundwater Potential ◽  
Geographic Information ◽  
Groundwater Potential Zones ◽  
System A

scholarly journals Modelagem espacial de áreas suscetíveis a inundações baseada em um modelo multicritério hibrido e Sistema de Informação Geográfica: um estudo de caso aplicado a bacia do Rio Bengalas

2020 ◽  
Vol 13 (3) ◽  
pp. 1145
Author(s):  
Fabiano Peixoto Freiman ◽  
Camila De Oliveira Carvalho
Keyword(s):  
Information System ◽  
Fuzzy Logic ◽  
Geographic Information System ◽  
River Basin ◽  
Analytical Hierarchy Process ◽  
Rio De Janeiro ◽  
Geographic Information ◽  
Flood Susceptibility ◽  
Hierarchy Process

A identificação de áreas suscetíveis a inundações é essencial para o gerenciamento de desastres e definição de políticas públicas. O objetivo deste trabalho é a apresentação de um método para identificação de áreas suscetíveis a inundações através da integração de informações geográficas provenientes de técnicas do Sensoriamento Remoto, as ferramentas do Sistema de Informação Geográfica (SIG), a lógica Fuzzy e a aplicação de Métodos de Análise Multicritério (MAM) Analytical Hierarchy Process (AHP). Para atingir o objetivo foi proposto um estudo de caso, localizado na Bacia do Rio Bengalas, nos municípios de Nova Friburgo e Bom Jardim (Região Serrana do Rio de Janeiro). A modelagem espacial multicritério foi realizada a partir da seleção de um conjunto de dados composto por informações geomorfológicas, hidrológicas e de uso e ocupação do solo. Como resultado, obteve-se um mapa de suscetibilidade a inundações para a região. A coerência do modelo gerado foi verificada a partir do histórico de inundações da bacia do Rio Bengalas. A metodologia, apresentou-se eficiente e adequada para a determinação de áreas suscetíveis a inundações, prevendo com sucesso a distribuição espacial de áreas com riscos a inundações.  Spatial modelling of flood-susceptible areas based on a hybrid multi-criteria model and Geographic Information System: a case study applied to the Bengalas River basin A B S T R A C TThe identification of areas susceptible to flooding is essential for disaster management and public policy making. The objective of this work is the presentation of a method for the identification of areas susceptible to floods through the integration of geographic information from Remote Sensing techniques, Geographic Information System (GIS) tools, Fuzzy logic and the application of Multicriteria Analysis Methods (MAM) Analytical Hierarchy Process (AHP). In order to achieve the objective, a case study was proposed, located in the Bengalas River Basin, in the municipalities of Nova Friburgo and Bom Jardim (Mountain Region of Rio de Janeiro). Multicriteria spatial modeling was performed by selecting a data set composed of geomorphological, hydrological and land use information. As a result, a flood susceptibility map was obtained for the region. The coherence of the generated model was verified from the flood history of the Bengalas River basin. The methodology was efficient and adequate for the determination of areas susceptible to floods, successfully predicting the spatial distribution of areas at risk of flooding.Keywords: flood susceptibility. Fuzzy logic. MAM. AHP. GIS. 

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