GIS-Based Multi-Criteria Approach for Flood Vulnerability Assessment and Mapping in District Shangla: Khyber Pakhtunkhwa, Pakistan

Abstract

Floods are considered one of the world’s most overwhelming hydro meteorological disasters, which cause tremendous environmental and socioeconomic damages in a developing country such as Pakistan. In this study, we use a Geographic information system (GIS)-based multi-criteria approach to access detailed flood vulnerability in the District Shangla by incorporating the physical, socioeconomic vulnerabilities, and coping capacity. In the first step, 21 essential criteria were chosen under three vulnerability components. To support the analytical hierarchy process (AHP), the used criteria were transformed, weighted, and standardized into spatial thematic layers. Then a weighted overlay technique was used to build an individual map of vulnerability components. Finally, the integrated vulnerability map has been generated from the individual maps and spatial dimensions of vulnerability levels have been identified successfully. The results demonstrated that 25% of the western-middle area to the northern part of the study area comprises high to very high vulnerability because of the proximity to waterways, high precipitation, elevation, and other socioeconomic factors. Although, by integrating the coping capacity, the western-central and northern parts of the study area comprising from high to very high vulnerability. The coping capacities of the central and eastern areas are higher as compared to the northern and southern parts of the study area because of the numerous flood shelters and health complexes. A qualitative approach from the field validated the results of this study. This study’s outcomes would help disaster managers, decision makers, and local administration to quantify the spatial vulnerability of flood and establish successful mitigation plans and strategies for flood risk assessment in the study area.

GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

“…The poor drainage systems of the study area almost completely clogged all the drainages during the rainy season, causing severe floods [35]. The District Peshawar was also one of the worst affected areas during the 2010 floods when the Kabul River and Budhni Nullah devastated most parts of the district [57,58]To generate this parameter, the primary data were collected from the field through a structured questionnaire to determine flood damages in the area. The collected raw data were computed in SPSS and transformed into percentages while dividing the study area into two zones (West zone and East zone).…”

“…The coping capacity or adaptive mechanism/capacity is a vital element of vulnerability assessment [61]. The coping capacities refer to the measures/actions taken by the local people using resources before, during, or after floods to minimize adverse effects [57,58]. According to Kienberger et al [62], adaptive capacity is a function of social capacity and resilience.…”

A systematic review of the flood vulnerability using geographic information system

“…The peak discharge was calculated using the SWAT model, and the peak discharge was included as one of the indications. The General Circulation Models (GCM) data were used to forecast future rainfall, while Landsat pictures were used to create land use and land (Singh and Pandey, 2021), (Hosseini et al, 2021) (Nazeer and Bork, 2021), (Hussain et al, 2021) (Pathak et al, 2020), (Hoque et al, 2019) (Mavhura et al, 2017), (Terti et al, 2015) (Eidsvig et al, 2014), (Zhang, 2009) Physical vulnerability Interdependency analysis, indicator methodology, decision-making trial method., Indicator based approach, morphometric parameters were derived from SRTM DEM data using (GIS), Weighted Sum Approach (WSA), Principal Component Analysis (PCA), and an Integrated Approach (IA), GIS-Based Multi-Criteria Approach, Geospatial Indicator-Based Approach and Participatory Analytical Hierarchy Process, Flood generating factors: slope, elevation, land use/land cover, drainage density, rainfall, and soil types were rated and collected to mark out flood vulnerability zones using (GIS), Regression and GIS conditioning factors include digital elevation model (DEM), Pearson's correlation, multicollinearity, and heteroscedasticity analyses (Singh and Pandey, 2021), (Hosseini et al, 2021), (Nazeer and Bork, 2021), (Hussain et al, 2021), (Vignesh et al, 2021), (Usman Kaoje et al, 2021), (Desalegn and Mulu, 2021), (Usman Kaoje et al, 2021), (Sami et al, 2020), (D'Ayala et al, 2020), (Chuang et al, 2020), (Yin et al, 2019), (Hoque et al, 2019), Sajjad, 2019), Hübl et al, 2016), (Al-Juaidi et al, 2018), (Hazarika et al, 2018), (Walliman et al, 2012) and, (Mehebub et al, 2015) Environmental…”

Análise de Susceptibilidade a Alagamento em Juazeiro do Norte

“…Embora o alagamento seja um desastre que não pode ser absolutamente evitado, o grau de consequências, efeitos adversos e a quantidade de perdas podem ser reduzidas por meio de análise da susceptibilidade a alagamento, prevendo áreas potencialmente alagadas (Ali et al, 2019). Nesse sentido, dados de sensoriamento remoto e sistema de informações geográficas (SIG) fornecem ferramentas que contribuem significativamente para a avaliação da vulnerabilidade ou susceptibilidade a alagamento de diversas áreas, como pode-se ver nos trabalhos de Vojtek e Vojteková (2019); Ali et al (2020); Domakinis et al (2020); Islam et al (2020); Al-Hinai e Abdalla (2020); Yariyan et al (2020); Wicaksono et al (2021); Hussain et al (2021). Por meio desses métodos é possível desenvolver banco de dados com maior precisão, aplicar técnicas de sensoriamento remoto em grandes áreas de difícil acesso e processar vários tipos de dados (Diaconu et al, 2021).…”

“…Concomitantemente, a Região Metropolitana do Cariri (RMC) encontra-se com um quadro vulnerável relacionado à gestão das águas pluviais, visto às intensas taxas de impermeabilização do solo e ocupação urbana ao redor de cursos d'água, trazendo variados níveis de degradação ambiental (Moura-Fé et al, 2019). Dos três municípios que compõem a RMC, Juazeiro do Norte possui a maior área urbanizada da região (Teles, 2018) e a quinta maior do estado (Pereira, 2013), sendo um grande fator condicionante para o alagamento (Hussain et al, 2021), visto que qualquer tipo de mudança no uso do solo resultante da urbanização leva a eventos de alagamento dado o aumento do coeficiente de precipitação escoada (Dammalage e Jayasinghe 2019). Nesse sentido, cabe ao presente estudo avaliar a problemática do alagamento no município de Juazeiro do Norte.…”