Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

Abrar MF ，Iman YE ，Mustak MB ，Pal SK ... -  《-》

A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India.

Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes. Remote sensing, GIS, and analytical hierarchy technique (AHP) were used to identify flood-prone zones and to determine the weights of various factors affecting flood risk, such as rainfall, distance to river, elevation, slope, land use/land cover, drainage density, soil type, and lithology. Four groups (zones) were identified by the flood susceptibility map including high, medium, low, and very low. These zones occupied 16.41%, 67.33%, 16.18%, and 0.08% of the area, respectively. Historical flood events in the study area coincided with the flood risk classification and flood vulnerability map. Regions situated close to rivers, characterized by low elevation, slope, and high runoff density were found to be more susceptible to flooding. The flood susceptibility map generated by the GIS-MCDA accurately described the flood-prone regions in the study area.

Bagyaraj M ，Senapathi V ，Chung SY ，Gopalakrishnan G ，Xiao Y ，Karthikeyan S ，Nadiri AA ，Barzegar R ... -  《-》

Application of geographical information system-based analytical hierarchy process modeling for flood susceptibility mapping of Krishna District in Andhra Pradesh.

Flooding is one of the most catastrophic natural disasters in terms of provoking socio-economic 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, and 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 very high, high, moderate, low, and very low susceptible, respectively. Following that, historical flood data was used to validate the accuracy of the generated flood susceptibility map. This shows that a maximum of 90% of the data points are within floodplain.

Penki R ，Basina SS ，Tanniru SR 《-》

Mapping flood susceptibility with PROMETHEE multi-criteria analysis method.

On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00-19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods.

Plataridis K ，Mallios Z 《-》

AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India.

Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approaches for alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R2 values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies.

Pathan AI ，Girish Agnihotri P ，Said S ，Patel D ... -  《-》