National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh

Siam, Zakaria Shams; Hasan, Rubyat Tasnuva; Anik, Soumik Sarker; Noor, Fahima; Adnan, Mohammed Sarfaraz Gani; Rahman, Rashedur M.; Dewan, Ashraf

doi:10.1080/10106049.2022.2063411

Cited by 23 publications

(4 citation statements)

References 75 publications

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“…Based on several inputs, including precipitation data, topography data, and river flow data, DNNs are utilized in flood forecasting to anticipate probable flood levels [35]. These models can offer helpful data for early warning systems, emergency response planning, and resource allocation during flood occurrences [131]. To map flood hazards, which requires locating flood-prone locations, DNNs can be utilized.…”

Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

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Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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“…After obtaining feature importance of individual resilience-related feature to the final clustering results, the community-resilience level is determined as follows: 1. Following Siam et al 63 , Xu et al 64 , and Yin and Mostafavi 20 's work community resiliencerelated features are normalized to reduce the impact of the difference of unit using Min-Max scaler: 𝑥 ′ = 𝑥 − 𝑥 𝑚𝑖𝑛 𝑥 𝑚𝑎𝑥 − 𝑥 𝑚𝑖𝑛 (17) where 𝑥 ′ is the scaled value which is in the range of [0,1].…”

Section: Community Resilience Level Determinationmentioning

confidence: 99%

Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features

Mostafavi,

kai

2023

Preprint

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Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience properties. However, present studies on community resilience focus primarily on vulnerability assessment and utilize index-based approaches, with limited ability to capture heterogeneous features within community socio-technical systems and their nonlinear interactions in shaping robustness, redundancy, and resourcefulness components of resilience. To address this gap, this paper presents an integrated three-layer deep learning model for community resilience rating (called Resili-Net). Twelve measurable resilience features are specified and computed within community socio-technical systems (i.e., facilities, infrastructures, and society) related to three resilience components of robustness, redundancy, and resourcefulness. Using publicly accessible data from multiple metropolitan statistical areas in the United States, Resili-Netcharacterizes the resilience levels of spatial areas into five distinct levels. The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies. Changes in community resilience profiles under urban development patterns are further examined by changing the value of related socio-technical systems features. Also, combined resilience-risk levels in each community are analyzed, and several communities are found to suffer from high risk and low resilience, which calls for special attention to resilience enhancement. Departing from the dominantly vulnerability-focused assessments, Resili-Net enables characterizing community resilience as an emergent property arising from nonlinear interactions among heterogeneous community features related to their socio-technical systems. Accordingly, the outcomes provide novel perspectives for community resilience assessment by harnessing machine intelligence and heterogeneous urban big data.

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“…Nonetheless, comprehensive approaches are those that account for interdependencies and interactions between hazards while addressing the uncertainties and limitations associated with hazard modeling and data availability (Talchabhadel et al, 2023). Among these assessment methods, the Fuzzy Analytical Hierarchy Process (FAHP) method has received attention recently (Siam et al, 2022). This decision-making approach is potent and versatile, finding applications in various fields, including economics, engineering, ecological management, and urban planning.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Multi-Hazard Risk Associated with Tropical Cyclones Using the Fuzzy Analytic Hierarchy Process Model

Sarker,

Adnan

2023

Preprint

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National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh

Cited by 23 publications

References 75 publications

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features

Evaluating Multi-Hazard Risk Associated with Tropical Cyclones Using the Fuzzy Analytic Hierarchy Process Model

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