Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

Pham, Quoc Bao; Ali, Sk Ajim; Bielecka, Elżbieta; Całka, Beata; Orych, A.; Parvin, Farhana; Łupikasza, Ewa

doi:10.1007/s11069-022-05336-5

Cited by 19 publications

(6 citation statements)

References 96 publications

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“…Unlike optical imagery, radar can penetrate clouds, making it ideal for flood mapping in regions prone to heavy cloud cover or persistent rainfall [71]. On the other hand, machine learning algorithms provide an objective and scalable approach to flood mapping [84][85][86]. Once the models are trained, they can be applied to large geographic areas, facilitating extensive flood susceptibility mapping [87,88].…”

Section: Discussionmentioning

confidence: 99%

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Hitouri,

Mohajane,

Lahsaini

et al. 2024

Remote Sensing

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Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.

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Section: Discussionmentioning

confidence: 99%

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Hitouri,

Mohajane,

Lahsaini

et al. 2024

Remote Sensing

Self Cite

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“…This can aid local governments and emergency services in better preparing for and responding to flood occurrences. Based on elements including elevation, geography, land use, and soil type, MLPs can be trained to recognize locations that are vulnerable to flooding [91]. Local governments may use maps of the flood risk created using these data to help them plan for and lessen the effects of floods.…”

Section: Multilayer Perceptron (Mlp)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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“…The selection of risk index varies by region and depends on the specific characteristics of the location [ 10 ]. One index could have a high degree of impact toward flood risk in one area but could be neglected in another area [ 38 ]. According to the actual conditions of hazard-formative factors, hazard-formative environments, hazard-affected bodies, and the capability for disaster prevention and reduction, starting from the two risk elements of hazard and vulnerability, this study selected 10 flood risk indexes for analysis: maximum 1-day rainfall amount (Rx1day), number of heavy rainfall days above 50 mm (R50mm), digital elevation model (DEM), slope (SL), land use pattern (LUP), distance to the river (DR), gross domestic product density (GDP), dynamic population (D-POP), building density (BD), and pipe network density (PD).…”

Section: Study Area and Data Descriptionmentioning

confidence: 99%

Urban Flood Risk Assessment Based on Dynamic Population Distribution and Fuzzy Comprehensive Evaluation

Chen

Liu

et al. 2022

IJERPH

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Floods are one of the most common natural disasters that can cause considerable economic damage and loss of life in many regions of the world. Urban flood risk assessment is important for urban flood control, disaster reduction, and risk management. In this study, a novel approach for assessing urban flood risk was proposed based on the dynamic population distribution, improved entropy weight method, fuzzy comprehensive evaluation method, and the principle of maximum membership, and the spatial distribution of flood risk in four different sessions or daily time segments (TS1–TS4) in the northern part of the Shenzhen River Basin (China) was assessed using geographic information system technology. Results indicated that risk levels varied with population movement. The areas of highest risk were largest in TS1 and TS3, accounting for 7.03% and 7.07% of the total area, respectively. The areas of higher risk were largest in TS2 and TS4, accounting for 4.54% and 4.64% of the total area, respectively. The findings of this study could provide a theoretical basis for assessing urban flood risk management measures in Shenzhen (and even throughout China), and a scientific basis for development of disaster prevention and reduction strategies by flood control departments.

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Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

Cited by 19 publications

References 96 publications

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

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

Urban Flood Risk Assessment Based on Dynamic Population Distribution and Fuzzy Comprehensive Evaluation

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