Measuring Vulnerability for City Dwellers Exposed to Flood Hazard: A Case Study of Dhaka City, Bangladesh

Huq, Md. Enamul; Shoeb, A. Z. M.; Javed, Akib; Shao, Zhenfeng; Hossain, Mallik Akram; Sarven, Most. Sinthia

doi:10.1007/978-3-030-45099-1_16

Cited by 3 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dhaka, the capital, is located in the central part of Bangladesh and at the confluence of three major rivers: the Brahmaputra, the Meghna, and the Ganges. The geographical location of the city makes it extremely susceptible to flooding [9,14].…”

Section: Study Areamentioning

confidence: 99%

“…Around two-thirds of the country has elevations <5 m above mean sea level [12]. Every year, it receives copious rainfall during the monsoon season and high river discharge along with intense rainfall results in floods of varying magnitudes [6], which are recurrent and lead to significant damages to lives and properties [8,9,[12][13][14]. The Bangladesh Water Development Board (BWDB) is primarily responsible for providing flood warnings [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Noor

Haq

Rakib

et al. 2022

Water

View full text Add to dashboard Cite

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.

show abstract

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Noor

Haq

Rakib

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…Flash floods are serious natural hazards in Dhaka, Bangladesh. People living in slums in this area also have lower levels of education (Huq et al 2020b). These low-educated people migrate from villages to the city to seek jobs and better lives.…”

Section: Education and Occupationmentioning

confidence: 99%

“…Vulnerability is differentiated socially and geographically. It is recognized that identifying and understanding the factors of vulnerability are imperative for assessing vulnerability (Chen et al 2019;Huq et al 2020b;Shao et al 2020;Zakour and Swager 2018). The vulnerability of a population to large-scale hazards can be reduced by applying disaster management efforts.…”

Section: Introductionmentioning

confidence: 99%

Measuring Vulnerability to Flash Flood of Urban Dwellers

Huq

Shao

Dughairi

et al. 2021

Natural Disaster Science and Mitigation Engineering: DPRI Reports

Self Cite

View full text Add to dashboard Cite

Flash floods are unexpected, localized flood events that occur when an exceptional amount of rain falls happens over a short period of time. In South Asia, it is mostly disastrous, for example, in 2017 flash floods killed approximately 1200 people from India, Nepal, and Bangladesh. However, it is also common in Dhaka megacity, Bangladesh due to its geographic location, monsoon climatic condition and surrounding rivers. Though it is impossible to avoid them, the losses and damages of hazards can be reduced effectively by using appropriate techniques. This study aims to determine the responsible factors and measure the household vulnerability to flash flood as a tool of mitigation. The study has been conducted based on primary data. Therefore, data were collected from both slum and non-slum population to cover the entire urban habitats. Data were collected with a structured questionnaire based on five factors (social, economic, institutional, structural, and environmental) of vulnerability to flash flood. The key feature of this paper is to provide an insight into real picture of vulnerability to flash flood for urban habitants. Moreover, this practical approach is useful to quantify hazard-induced vulnerabilities not only for Dhaka megacity but also for other cities of the globe.

show abstract

“…1). Economic vulnerability leads to social vulnerability (Ahsan and Warner, 2014;Huq et al, 2020). Alternatively, the impact of social vulnerability causes demographic and institutional vulnerability.…”

Section: Flood-vulnerability Factorsmentioning

confidence: 99%

Assessing vulnerability for inhabitants of Dhaka City considering flood-hazard exposure

et al. 2020

Self Cite

View full text Add to dashboard Cite

Global flood hazard is gradually increasing. Though it is impossible to avoid them, losses and damage of hazards (e.g., floods, cyclones, and earthquakes) could be efficiently reduced by reducing household vulnerability with appropriate measures. This study aims to quantitatively measure the household vulnerability of flood hazards as a mitigation tool. It also proposed a unique approach to quantify flood-hazard household vulnerability, and shows its application in the flood prone city of Dhaka as an example case. Data were collected from both slum and non-slum areas to cover the entire urban habitat, and to compare their level of flood vulnerability. A total of 300 households were surveyed by structured questionnaire on the basis of five factors (economic, social, environmental, structural, and institutional) of flood vulnerability. The analytical hierarchy process (AHP) was applied to measure individual household vulnerability scores by using the relative weightage of variables and indicators with proper standardisation. Analytical results demonstrated that 63.06% slum and 20.02% non-slum households were highly vulnerable to floods. In addition, this paper determined and assessed responsible factors for household flood vulnerability in Dhaka. For structural vulnerability, results exhibited that 82% of slum households were highly vulnerable, and 95.3% of non-slum households were moderately vulnerable. Socially, 67.3% of slum and 78.7% of non-slum households were moderately and low-vulnerable. The majority of slum and non-slum households (84% and 59.3%, respectively) showed high and moderate vulnerability with respect to economic vulnerability. Moreover, 69.3% of slum and 65.3% of nonslum household institutional vulnerability levels were high. Of slum inhabitants, 63.3% were environmentally at high risk, and 78% of non-slum habitats were in the low-vulnerability category. However, as an effective tool to measure location-specific vulnerability, it is applicable for the measuring vulnerability of other cities in the world with proper customisation. On the basis of this study, future research could be conducted with more factors, variables, and indicators of human vulnerability to natural or artificial hazards/disasters. Future work may provide a better reflection of the vulnerability status of single/multiple hazard(s)/disaster(s).

show abstract

Measuring Vulnerability for City Dwellers Exposed to Flood Hazard: A Case Study of Dhaka City, Bangladesh

Cited by 3 publications

References 14 publications

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Measuring Vulnerability to Flash Flood of Urban Dwellers

Assessing vulnerability for inhabitants of Dhaka City considering flood-hazard exposure

Contact Info

Product

Resources

About