Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India

Sar, Nityananda; Chatterjee, Soumendu; Adhikari, Manik Das

doi:10.1007/s40808-015-0039-9

Cited by 67 publications

(32 citation statements)

References 51 publications

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“…The advantage of AHP is that the method is designed to solve complex problems involving multiple criteria, and it combines qualitative and quantitative factors (Saaty ). Previous studies (Kiker et al ; Sar et al ; Banerjee et al ) have used the AHP model to predict landslides vulnerability, and the method has proved to be very effective and valuable for vulnerability assessment and mapping.…”

Section: Methodsmentioning

confidence: 99%

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Nahayo

Ndayisaba

Karamage

et al. 2019

Integr Envir Assess & Manag

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Landslides are among hazards that undermine the social, economic, and environmental well‐being of the vulnerable community. Assessment of landslides vulnerability reveals the damages that could be recorded, estimates the severity of the impact, and increases the preparedness, response, recovery, and mitigation as well. This study aims to estimate landslides vulnerability for the western province of Rwanda. Field survey and secondary data sources identified 96 landslides used to prepare a landslides inventory map. Ten factors—altitude, slope angles, normalized difference vegetation index (NVDI), land use, distance to roads, soil texture, rainfall, lithology, population density, and possession rate of communication tools—were analyzed. The Analytical Hierarchy Process (AHP) model was used to weight and rank the vulnerability conditioning factors. Then the Weighted Linear Combination (WLC) in geographic information system (GIS) spatially estimated landslides vulnerability over the study area. The results indicated the altitude (19.7%), slope angles (16.1%), soil texture (14.3%), lithology (13.5%), and rainfall (12.2%) as the major vulnerability conditioning parameters. The produced landslides vulnerability map is divided into 5 classes: very low, low, moderate, high and very high. The proposed method is validated by using the relative landslides density index (R‐index) method, which revealed that 35.4%, 25%, and 23.9% of past landslides are observed within moderate, high, and very high vulnerability zones, respectively. The consistency of validation indicates good performance of the methodology used and the vulnerability map prepared. The results can be used by policy makers to recognize hazard vulnerability lessening and future planning needs. Integr Environ Assess Manag 2019;00:000–000. © 2019 SETAC

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Section: Methodsmentioning

confidence: 99%

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Nahayo

Ndayisaba

Karamage

et al. 2019

Integr Envir Assess & Manag

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“…The basic model approach is the conversion of all the thematic layer attributes into a normalized weighted raster as per the water holding capacity through AHP and Multi Criteria Decision Support System (Nityananda Sar et al, 2015).…”

Section: Tirkey Et Al 2013)mentioning

confidence: 99%

Review of Significant Works in Assessment of Ground Water Vulnerability in India

2017

IJAERD

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Abstract-Ground water pollution is the artificially induced degradation of natural ground water quality. In India, water pollution is a serious problem as almost 70% of its surface water resources and the ground water reserves are already contaminated by biological, organic and inorganic pollutants. Vulnerability mapping is a technique for quantifying the sensitivity of the resource to its environment, and as a practical visualization tool for decision-making. In recent years, Geographic Information Systems (GIS), Optimised DRASTIC index method, neural networks and fuzzy logic techniques and other mathematical and statistical methods have been used for the assessment of GW vulnerability across the world using several hydrological database of concerned basin. This paper reviews the significant research work of groundwater vulnerability assessment done in different states of India.

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“…[28,39,[44][45][46][47][48][49]. In addition, hydrologic-and hydrodynamic-based mathematical simulation models are among the main methods for evaluating urban waterlogging risks [12,37,40,41,50,51].…”

Section: Introductionmentioning

confidence: 99%

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Tran

Dang

et al. 2020

Water

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In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

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Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India

Cited by 67 publications

References 51 publications

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Review of Significant Works in Assessment of Ground Water Vulnerability in India

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

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