Vulnerability assessment of flood-affected locations of Bangalore by using multi-criteria evaluation

Prasad, Neeraj; Narayanan, Priya

doi:10.1080/19475683.2016.1144649

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…Multi-Criteria Decision Analysis (MCDA) methods provide decision support tools for dealing with natural resource management issues and other complex decision constellations (Myagmartseren et al, 2017). These methods have been repeatedly applied with the combination of GIS in mapping and assessing ood risk (Lin et al, 2019;Prasad and Narayanan, 2016;Saha and Agrawal, 2020). The GIS-based MCDA inputs data and combines the output into weighted maps based on the ability of GIS to manage geospatial data (data acquisition, storage, retrieval, manipulation, and analysis) and the exibility of MCDA to combine geographical data with value-based information or the decision maker's preferences into unidimensional values of alternative decisions (Stefanidis and Stathis, 2013;Wang et al, 2011;Yahaya et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

GIS-based multi-criteria analytical hierarchy process modelling for urban flood vulnerability analysis, Accra Metropolis

et al. 2023

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A series of devastating ood events within the Accra Metropolis over the last decades have demonstrated the urgent need for urban resilience. The present work aimed to develop a GIS-based model to analyse and assess oods in the Accra Metropolis. The framework is based on the Multi-Criteria Analytical Hierarchy Process within the GIS platform. Relevant thematic layers including LULC, Elevation, Slope, Soil, and Drainage density were generated within the GIS environment. In the context of objective weight assignments, the AHP algorithm was successfully applied to create ood hazard indices, and then the urban ood risk maps were constructed for 2007, 2010, 2015 and 2020. The ndings revealed that areas under high and very high ood risk have increased from 55.163-60.043% over the last decade and a half.A ood inventory map was produced by randomly mapping 256 ood test locations to determine the model's performance. These locations consisted of 128 ooded and 128 non-ooded points extracted from historical ood data, ground truth data and high-resolution satellite imagery. The computed Receiver Operating Characteristic (ROC) curve showed an Area Under the Curve (AUC) value of 0.916, indicating an excellent correlation between the analysed ood risk areas and the ground truth data. Findings from the study will assist decision-makers in formulating medium-long term mitigation measures to reduce oodrelated damages and employ proper future land-use planning.

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Section: Introductionmentioning

confidence: 99%

GIS-based multi-criteria analytical hierarchy process modelling for urban flood vulnerability analysis, Accra Metropolis

et al. 2023

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“…(Banerjee et al 2020;Kumar & Bhattacharjya 2021). With the advent of GIS, these data can be easily processed and analyzed to estimate hydrological modeling parameters (Prasad & Narayanan 2016;Seekao & Pharino 2016;Kumar & Himanshu 2017). ArcGIS is a more advanced tool and provides a larger user base with better interface (Khatami & Khazaei 2014).…”

Section: Introductionmentioning

confidence: 99%

HEC-HMS based rainfall-runoff model for Punpun river basin

Ranjan

Singh

2022

Water Practice and Technology

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Various hydrological models were used in different river basins to simulate the runoff on available rainfall, land use and soil property data. HEC-HMS model is used by several researchers to estimate the water potential of the basin through rainfall-runoff modeling. In this study, rainfall-runoff model for the Punpun river basin has been developed using HEC-HMS. Daily rainfall and runoff data from the year 2005 to 2017 were used for the development of model. ArcGIS has been used to analyze the hydrological parameters, preparation of LULC, soil and slope maps for the computation of Curve Number as input into the HEC-HMS model. Daily, Monthly and Monsoonal rainfall-runoff models have been developed. The performance of all the models have been evaluated using statistical indices – Coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS) and RMSE-Observations Standard deviation Ratio (RSR). R2 and NSE values for all the models are greater than 0.75 and PBIAS is less than 10, it shows very good results from all the models except daily model, in which NSE values are less than 0.75. Based on statistical indices, the monthly model performs better as compared to daily and monsoonal models.

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Flood Risk Assessment for a Medium Size City Using Geospatial Techniques with Integrated Flood Models

Natarajan

Radhakrishnan

2021

Springer Climate

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Vulnerability assessment of flood-affected locations of Bangalore by using multi-criteria evaluation

Cited by 11 publications

References 13 publications

GIS-based multi-criteria analytical hierarchy process modelling for urban flood vulnerability analysis, Accra Metropolis

GIS-based multi-criteria analytical hierarchy process modelling for urban flood vulnerability analysis, Accra Metropolis

HEC-HMS based rainfall-runoff model for Punpun river basin

Flood Risk Assessment for a Medium Size City Using Geospatial Techniques with Integrated Flood Models

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