Urban flood vulnerability zoning of Cochin City, southwest coast of India, using remote sensing and GIS

Sowmya, K.; John, C. M.; Shrivasthava, N. K.

doi:10.1007/s11069-014-1372-4

Cited by 95 publications

(37 citation statements)

References 14 publications

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“…However, the expansion of cities is part of development, and problems arise due to rapid urbanization without proper planning, which in turn increases vulnerability and exposure to risk due to the occupation of the floodplain [29,[83][84][85]. In a study carried out in various districts of Latin American cities, it was deduced that in different cities, exposure to floods increased, and mostly in areas of low and mid-range [17,37].…”

Section: Discussionmentioning

confidence: 99%

“…For variables 1-5, demographic information by city block was used from the 2002 census (National Statistics Institute) [74,75]. Vulnerability scores were determined on a scale of 0-10 [29], where the maximum value corresponded to a high level of vulnerability. Individual classes of variables 1-4 were normalized using Equation (5) [41].…”

Section: Analysis Of River Disastersmentioning

confidence: 99%

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Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011)

et al. 2017

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Abstract:Urban flooding is an increasing problem in developing countries, thus understanding flood dynamics is necessary to establish guidelines on urban development and flood management. This paper studied the lower reaches of the Andalien River basin, situated on the coast of the Greater Concepcion Metropolitan Area (CMA), the second most urbanized area in Chile. This basin experienced a record 21 flood events in the last 50 years. Patterns in the evolution of urban growth were evaluated using a historical analysis ) that specifically focused on how these patterns interact with zones that are prone to flooding over different return periods. Our model applied to the CMA, but is sufficiently transversal to be applied in similar areas. It was determined that the built-up area increased by 1457 ha and had an urban form vulnerable to future flooding due to its location on landforms exposed to flood risk, with altitudes of <8 m and slopes of <0.5 • . The bankfull channel of the Andalien River lost 18.4% of its area. Upstream, management of the river decreased smaller floods for a return period of two, five, and 10 years; however, for a return period of 500 years, risk remained at high levels. These findings help to rethink and improve urban planning and the response of Chilean cities in relation to flood hazard.

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Section: Discussionmentioning

confidence: 99%

Section: Analysis Of River Disastersmentioning

confidence: 99%

Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011)

et al. 2017

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“…Thus, slope was most frequently used as a factor related to flood among the topographical factors (Pradhan 2010;Tehrany et al 2014;Bui et al 2016;Khosravi et al 2016, Marconi et al 2016, Rahmati et al 2016bSeekao & Pharino 2016b;Youssef et al 2016b). Additionally, DEM (Pradhan 2010;Tehrany et al 2014;Sowmya et al 2015;Bui et al 2016;Marconi et al 2016;Rahmati et al 2016b;Youssef et al 2016b), distance to river (Bui et al 2016;Khosravi et al 2016;Marconi et al 2016;Rahmati et al 2016b;Youssef et al 2016b), TWI and SPI (Bui et al 2016;Khosravi et al 2016) and plan curvature (Bui et al 2016;Khosravi et al 2016;Youssef et al 2016b) are used to evaluate floods from topographical factors. SLF is also included to spatial datasets since it is related to water drainage (Borrelli et al 2014) with soil-drainage factor (Tehrany et al 2014;Seekao & Pharino 2016a;Youssef et al 2016b).…”

Section: Spatial Datasetsmentioning

confidence: 99%

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Lee

Kim

Jung

et al. 2017

Geomatics, Natural Hazards and Risk

289

128

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Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

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“…After a flood event, drainage plays an important role to evacuate flood waters, with an increase in drainage density reducing the vulnerability from the affected area; therefore, it has been used as a proxy for urban flood vulnerability assessment [103][104][105][106]. In contrast, a higher river network density ratio indicates a high vulnerability due to the relatively larger amount of flood-prone regions during a flood event [95].…”

Section: Othersmentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods.While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery.2 of 30 threats, to reduce own overall vulnerability, and to allow the community to recover from adverse impacts when they occur. Decades of disaster research offer extensive findings in this respect [4][5][6][7]. Remote sensing (RS)-as an effective and rapid tool for monitoring large areas-is essential for the acquisition of geospatial data, which in turn constitutes the basis for risk assessment and management. RS is widely used for various aspects of the DRM, ranging from vulnerability [8] to rapid damage assessments [9], for diverse areas ranging from coastal ecosystems [10] to complex urban settings [11], and for disasters as diverse as landslides [12,13] or cyclones [14].Numerous methods have been developed to extract information from RS data to identify, characterize, or quantify different phases of the disaster risk cycle: response, recovery, prevention/mitigation, and preparedness [15]. However, early studies predominantly considered the physical side of the assessments for both ...

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Urban flood vulnerability zoning of Cochin City, southwest coast of India, using remote sensing and GIS

Cited by 95 publications

References 14 publications

Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011)

Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011)

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

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