Location-allocation modeling for emergency evacuation planning with GIS and remote sensing: A case study of Northeast Bangladesh

Rahman, Mohd Tadza Abdul; Chen, Ningsheng; Islam, Monirul; Dewan, Ashraf; Pourghasemi, Hamid Reza; Washakh, Rana Muhammad Ali; Nepal, Nirdesh; Tian, Shufeng; Hamid, Faiz; Alam, Mehtab; Ahmed, Naveed

doi:10.1016/j.gsf.2020.09.022

Cited by 60 publications

(38 citation statements)

References 81 publications

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“…The validation and performance evaluation of the proposed model was carried out through the receiver operating characteristic (ROC) curve and area under the curve (AUC) using historical damage locations [66,74]. For validation, 142 sites (71 flood-damaged locations and 71 non-damaged locations) were considered from historical flood reports.…”

Section: Model Validation and Performance Evaluationmentioning

confidence: 99%

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

et al. 2021

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Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.

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Section: Model Validation and Performance Evaluationmentioning

confidence: 99%

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

et al. 2021

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“…(2) L-A model. The use of L-A models provides one approach to achieve the most effective allocation of public facilities [39]; these models have been successfully applied to determine the locations of educational [40], medical [41], and emergency facilities [42], as well as fire stations [11] and other services. The goal of L-A models is to locate the facilities in a way that supplies the demand points most efficiently, and it is a twofold problem that simultaneously locates facilities and allocates demand points to the facilities.…”

Section: The Spatial Optimization Of Fire Stationsmentioning

confidence: 99%

Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing

Wang

Sun

et al. 2021

IJGI

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The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities.

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“…g. Normalized Difference Vegetation Index (NDVI): Intensity of flood and flood susceptibility has been associated with vegetation cover (Ajin et al 2013;Kaur et al 2017;Rahman et al 2021). Normalized Difference Vegetation Index or NDVI has been derived from LANDSAT-8 imagery in Arc GIS 10.5 software (evaluation version) using the raster calculator tool.…”

Section: Land Use and Land Covermentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

Preprint

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Flood is a typical natural disaster which every year causes enormous damage to the natural environment, buildings and victims worldwide. The efficiency of flooding depends upon numerous criteria: flood strength, amplitude, frequency, flow time, changes in plan and river geometry, etc. In this research, three statistical models such as the frequency ratio, Shannon's entropy, and weight of evidence have been used to identify flood susceptibility regions in the Kopai River Basin. In order to run these statistical models, flooded and non-flooded pixels were employed. The outcome of these three models demonstrates that the upper reach of the basin is characterised by non-flooded zones and the bottom reach of the basin is high flooded zones. The lower part of the basin is therefore more vulnerable than that of the upper portion. 11 thematic layers are employed to get the final output of flood susceptibility zones. e.g., soil type, normalised vegetation difference index, normalised water differential index, river lift, slope, drainage density, land usage and land cover, rainfall, soil moisture indices, surface roughness. 370 sample points have been taken as test data and finally receiver operating curves have been plotted for testing the degree of accuracy. The result of the validation reveals that the accuracy of the frequency ratio is 96.5%. Shannon entropy is 91.2% accurate and the weight of evidence is 97.1% accurate. Weight of evidence is therefore the best model for flood susceptibility zones identification for this Kopai basin.

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Location-allocation modeling for emergency evacuation planning with GIS and remote sensing: A case study of Northeast Bangladesh

Cited by 60 publications

References 81 publications

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

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

Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

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