Flood Risk Assessment Model Research Articles

Evaluating the Enhanced Bathtub Model for Coastal Flood Risk Assessment in Table Bay, South Africa

ABSTRACTCoastal zones are susceptible to increasing pressures from urban development and natural hazards, such as storm events, climate change, and rising sea levels. The GIS‐based enhanced bathtub model (eBTM) enables the identification of areas at risk of flooding as a baseline for disaster management and coastal adaptation. This study aims to establish the methodological robustness of the eBTM for coastal flood modeling, by analyzing eight sites flooded during a recent storm event in Table Bay, Cape Town by comparing eBTM outputs with observed flood extent data collected after the storm. The validation showed that for 74% of the 332 validation points the spatial modeling error was < 6 m and for 56% below 3 m. The root mean square error for the model was 4.88 m, indicating an acceptable level of accuracy of the eBTM outputs for coastal risk assessments where more sophisticated models are unavailable.

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Developing a flood risk assessment model with genetic algorithm-based weights

To reduce flood risk efficiently within constrained disaster prevention budgets, governments employ economic analyses and qualitative flood risk assessments. However, conventional methods, such as entropy weight and the Analytic Hierarchy Process method, have limitations in terms of the accuracy of the flood risk index. Here, we overcome these limitations by applying a genetic algorithm (GA) – an optimization method mimicking a natural selection process and biological genetic evolution. We developed a new flood risk index by using GA to calculate weights to indicators associated with four items (Hazard, Exposure, Vulnerability, and Capacity) for 161 Korean cities and counties from 2016 to 2021. The indicators (number of buildings, farmland area, dependent population, etc.) for the Exposure and Vulnerability items were reflected in the evaluation only for damaged targets directly exposed to flood risk, using grid cells of indicators overlaid on the flood risk map. Our GA-based method aimed to optimize each indicator’s weights to minimize errors between damage rankings and flood risk index rankings. Results show that our method reduced errors by 21.42 % during 2016–2021, outperforming traditional methods. Therefore, it is easy to identify municipalities that lack disaster prevention capabilities and are vulnerable to flood risk by comparing flood risk indices under the same conditions, such as maximum rainfall index. Our proposed method could better aid local government in decision-making for flood risk mitigation by allocating constrained budgets efficiently.

AI Based Urban Resilience Planning: Opportunities and Challenges

Urban areas across the globe are confronting escalating flood risks, a crisis exacerbated by the effects of climate change, which necessitates precise hyper-localized risk assessments. This research introduces the application of the AI for resilient cities model for flood risk assessment, focusing specifically on the vulnerable area of Penthakata, located in the coastal city of Puri, Odisha. The hyper-local evaluation of associated flood risks is carried out, particularly at the building level. Leveraging cutting-edge geospatial technology, deep learning methodologies, and multi-parameter analysis, this study offers valuable insights into the flood vulnerabilities in the region. Additionally, the research emphasizes the integration of technology with community volunteers and local knowledge, highlighting the essential role of grassroots-level efforts in effective disaster management. The model is one of its kind combining advanced AI technology with community engagement, the study contributes to a holistic and localized approach to strengthen adaptive capacity in the face of increasing flood risks. The findings offer a compelling case for the adoption of hyperlocal risk assessments for urban and rural areas to be better informed of flood risks, prepared for potential disasters, and implement more effective mitigative measures. Ultimately, it aims to safeguard the lives and livelihoods of vulnerable communities of various regions, offering a model for spatial environmental and community resilience.

A High‐Performance Coupled Human And Natural Systems (CHANS) Model for Flood Risk Assessment and Reduction

AbstractIn recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high‐performance hydrodynamic model with an agent‐based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real‐time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km2 Eden Catchment in England, demonstrating its ability to predict interactive flood‐human dynamics and assess flood impact at the household‐level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short‐term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans.

A modified geographical weighted regression model for better flood risk assessment and management of immovable cultural heritage sites at large spatial scales

With the increase in extreme climatic events globally in recent years, the increased frequency of flood hazards has had a great impact on immovable cultural heritage sites (ICHs) due to their prolonged exposure to the disaster environment. This poses a risk management challenge, especially on large scales. Most existing flood risk assessment models for ICHs are determined using common natural hazard methods directly and focus less on the characteristics of ICHs. In this paper, we propose a modified geographical weighted regression (MGWR) model to assess flood risk at ICHs, and this model considers the spatial and age properties of the ICHs. These two properties were used for the construction of the weight matrix in the MGWR model. Eleven selected indices and loss survey data with 417 sample points, including 5 types of ICHs, were utilized for model training and testing in Shanxi Province, China. The results showed that the MGWR model had good accuracy with an R2 of 0.928. A comparison between the MGWR and normal GWR models indicated that the accuracies of the older ICHs improved more in the MGWR than in the GWR. We also found that the proposed model performed better than the normal GWR model using age as an index. Moreover, in comparison with three machine learning methods (decision tree, logistic regression, and random forest), the MGWR model still performed better and was less limited by the number of training samples. This paper provides evidence that the characteristics of ICHs are crucial in the construction of flood risk assessment models, and the proposed model can benefit the risk management of various types of ICHs at large spatial scales.

Application of Delaunay adaptive mesh refinement in flood risk assessment of multi-bridge system with short distance

To ensure bridge safety, the flood risk analysis is significant important. However, due to the small size and large number of piers in the short-distance multi-bridge system, the extremely long calculation time and low efficiency of the numerical model are induced by the small mesh size and large mesh number. In this paper, a flood risk assessment model of the multi-bridge system with short distance was established to improve the calculation efficiency based on the finite volume method combined with the Delaunay mesh adaptive refinement method. The calculated water level with refined and non-refined mesh was compared with the experimental data of a partial failure dam break test case and Shukry experiment of open channel bend flow. The calculated water level results are in good agreement with the experimental data. In addition, the mesh refinement model improved the calculation efficiency by more than 73% with ensuring the calculation accuracy. Finally, the flood risk of a real multi-bridge system with short distance was evaluated by using the numerical model. The calculated results shown that, different from the general flow law, the water level in the upstream and downstream channel of Bridge 2 rose with a maximum difference value of 0.326 m while the water level in the far downstream channel of Bridge 2 dropped result from the construction of Bridge 2 on the basis of the Bridges 1, 3 and 4. The construction of Bridge 2 also increased the flow velocity around Bridge 3 with maximum 0.013 m/s. This study provides a new tool and technical reference for flood risk analyses of similar multi-bridge system with short distance.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods

Against the backdrop of global warming and rising sea levels coupled with increasing urbanization, flood risks for plain cities have intensified. This study takes Liaocheng City as its research object and constructs a regional flood risk assessment model based on a combination of subjective and objective multi-weight methods. The model sets weights according to different return periods from three perspectives: the severity of disaster-causing factors, the exposure of disaster-prone environments, and the vulnerability of disaster-bearing bodies. It also uses a subjective–objective combination of weights for the severity of disaster-causing factors, adopts CRITIC-entropy weights for the exposure of disaster-prone environments and vulnerability of disaster-bearing bodies, and adopts AHP subjective weights for the criterion layer. Based on GIS spatial analysis technology, the examination and zoning of flood disasters at a county scale were carried out. The results show that, unlike the existing weighting methods and machine learning methods, this multi-weight combination method can simultaneously avoid the subjectivity of the results and the uncertainty of parameters, thus enabling more accurate decision-making results to be obtained. The spatial distribution of the comprehensive risk is high in the central and western parts and relatively low in the south and north, while the area characterized by very high risk is concentrated in Dongchangfu District and Guanxian County. With the gradual increase in return periods, the overall spatial distribution of medium-to-very-high-risk areas in risk zoning gradually shrinks, and the spatial distribution of very-high-risk areas gradually moves south but maintains a stable distribution rule. Flood risk assessment is an important basic process for disaster prevention and mitigation in plain cities, and the results of this study can provide a reference for similar plain cities.

Projecting Flood Risk Dynamics for Effective Long‐Term Adaptation

AbstractFlood losses have steadily increased in the past and are expected to grow even further owing to climate and socioeconomic change. The reduction of flood vulnerability, for example, through adaptation, plays a key role in the mitigation of future flood risk. However, lacking knowledge about vulnerability dynamics, which arise from the interaction between floods and the ensuing response by society, limits the scope of current risk projections. We present a socio‐hydrological method for flood risk assessment that simulates the interaction between society and flooding continuously, including changes in vulnerability through collective (structural) and private (non structural) measures. Our probabilistic approach quantifies uncertainties and exploits empirical data to chart risk dynamics including how society copes with flooding. In a case study for the commercial sector in Dresden, Germany, we show that increased adaptation is necessary to counteract the expected four‐fold growth in flood risk due to transient hydroclimatic and socioeconomic boundary conditions. We further use our holistic approach to identify solutions for effective long‐term adaptation, demonstrating that integrated adaptation strategies (i.e., combined structural and non structural measures) can reduce the average risk by up to 60% at the study site. Ultimately, our case study highlights the benefit of the model for robust flood risk assessment as it can capture unintended, adverse feedbacks of adaptation measures such as the levee effect. Consequently, our socio‐hydrological method contributes to a more systemic and reliable flood risk assessment that can inform adaptation planning by exploring the possible system evolutions comprehensively including unlikely futures.

A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems

Flood mapping is essential for managing and mitigating the risks associated with flood events. This study integrates weighted overlay analysis and analytical network process to create a flood susceptibility map. Twelve factors were selected to assess flood risk in the region, and an analytical network process was used to derive weights of factors. Weighted overlay analysis was then used to apply weights to the flood conditioning factors to prepare the flood susceptibility map. The most prominent historical flood extent was derived by using Landsat 8, Sentinel-2 and Sentinel-1 satellite imageries, spectral indices and decision tree model to generate a flood inventory map. The flood susceptibility map was assessed for accuracy using the flood inventory map, achieving an overall accuracy rate of 88.3 %. The primary factors affecting flood risk were elevation, slope, and distance from the stream, with high-risk areas mainly located downstream. To develop the flood risk assessment model, the essential factors were classified into four main categories: topographical, environmental, hydrological, and anthropological. All possible input combinations were checked using the outlier robust extreme learning machine for one to twelve input variables, and the best model was selected using the outlier robust extreme learning machine with the input combination of five variables (i.e., normalizes difference vegetation index, geology, precipitation, elevation, and slope). Based on the Sixth Intergovernmental Panel on climate change report, the developed outlier robust extreme learning machine was used to project potential flood areas in the next 78 years. Based on the different scenarios, the results indicated that 10.93 %, 8.55 %, 8.09 %, 11.99 %, and 16.28 % of all areas are projected to be highly and very highly flood susceptible. The tools and methods outlined in this study can assist decision-makers in preparing for and responding to floods. Moreover, the method developed in this study can be implemented in watersheds and urban areas and help manage flood risk in urban drainage systems.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Study on multiscale-multivariate prediction and risk assessment of urban flood

Few studies have explored the impact of drivers on urban flood at multi-time scales, and few studies have assessed the urban flood risk based on accurate description and quantification of the intrinsic correlation between urban flood and its drivers. Therefore, this study develops a multiscale-multivariate prediction model for flood volume (FV) using wavelet transform, and a flood risk assessment model based on the joint distribution of FV and its key drivers using the Copula method. The downtown area of Zhengzhou City, a typical city in North China, is taken as the study area. The results reveal that land use change is the key driver for variation of urban rainstorm flood, and rapid urbanization led to the variation observed in 2005. The impact of land use change on FV primarily manifests at the long cycle scale, and the multiscale-multivariate prediction model demonstrates effective simulation (NSE = 0.957, R2 = 0.958, MAPE = 13.47%) and prediction capabilities (relative errors are all below 20%). Taking the FV exceeding the threshold corresponding to the frequency of 37.5% as an example, the maximum conditional risk probability under nine combinations of key drivers is 88.34%, while the minimum probability is only 6.83%, intuitively indicating the impact of rainstorm and urbanization on this risk. These findings can provide technical references for urban flood forecasting, urban water resources management and urban development planning.

Flood risk assessment of the naeseongcheon stream basin, Korea using the grid-based flood risk index

Study RegionNaeseongcheon Stream Basin, Korea Study focusWe conducted a flood risk assessment using grid data for each indicator and a flood risk map. Using a grid-based flood risk map, grid data for the nine indicators included in the Hazard, Exposure, and Vulnerability items from 2016 to 2020 were selected and indexed by scoring each quantile interval. The entropy weight and Euclidean distance were then applied to calculate the grid-based item indices and flood risk index. Finally, the Expected Annual Damage (EAD) ranking calculated using the Korean flood risk assessment model was compared with the flood risk assessment ranking using grid data for 17 sub-basins in the Naeseongcheon Stream Basin for 2018. New hydrological insights for the regionThe analysis showed that the absolute error was improved by the application of the flood damage rate and selection of the grid cells including damage targets. When comparing the flood risk assessment ranking of each of the 17 sub-basins with the EAD ranking, the mean absolute error was 1.882, and the root mean square error was 3.009. This confirms that qualitative flood risk assessment using grid data and flood risk maps can provide as much basin-level risk analysis as quantitative flood risk assessment. Therefore, the method presented in this study can be utilized as a useful decision-support tool for the government’s basin-level disaster prevention projects.

Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China

The assessment of urban flood risk plays a vital role in disaster prevention and mitigation. This work aims to assess the dynamic risk of urban flood triggered by population movements through dividing urban functional zoning from the perspective of collective cognition. Firstly, the urban functional areas are identified using Points of Interest data and then the population movements mobile is detected based on functional areas using mobile signaling big data. Then, one-dimensional and two-dimensional hydrodynamic models are employed to simulate the 50-year flood scenario in Futian District, Shenzhen. Finally, a spatio-temporal dynamic assessment model for urban flood risk is constructed based on the extent of inundation, water depth, population density, and the disaster-bearing capacity of functional areas. The research findings are as follows: (1) Futian District's urban planning showcases harmonious integration of single-function and mixed-function areas. Utilizing the 50% perception standard efficiently identifies distinct functional types across diverse urban zones. The results are highly consistent with the actual situation. (2) During morning peak hours, the population exhibits a nuanced pattern of dispersal, concentration, and transition. Lunchtime witnesses multiple central clusters forming and gradually dispersing, while the evening peak witnesses population regrouping, covering broader geographical extents. Dynamic utilization of functional areas and mobile phone signaling data outperforms static population metrics, offering deeper insights into the complexities of human activity. (3) Between 12:00 and 13:00, lunchtime movements lead to a surge of 6 high-risk zones in the central area and 5 in the Meiling area. The dynamic flood risk assessment model, based on functional area delineation, effectively identifies disparities and fluctuations in flood risk across diverse functional areas during rainfall scenarios, ensuring heightened precision and accuracy in risk assessment.

Urban flood risk assessment using AHP and geospatial techniques in swat Pakistan

The rapid urbanization and changing climate patterns in Swat, Pakistan have increased the vulnerability of urban areas to flood events. Accurate assessment of flood risk is crucial for effective urban planning and disaster management. In current research study flood hazard index was developed using analytic hierarchy process (AHP) technique in combination with the geographical information system (GIS) environment in Swat, Pakistan. The study integrates various data sources, including topographic maps, land use/land cover information, rainfall data, and infrastructure data, to develop a comprehensive flood risk assessment model. The weights obtained from the AHP analysis are combined with geospatial data using a geographic information system (GIS) to generate flood risk maps. The flood hazard levels were categorized into five distinct classes: very low, low, moderate, high, and very high. Using the GIS-AHP approach, higher weights were assigned to rainfall, distance to river, elevation, and slope in comparison to NDVI, TWI, LULC, curvature, and soil type. The flood hazard map was then reclassified for each parameter. By overlaying these maps, it was determined that 5.6% of the total area is classified as very high flood risk, 52% as high risk, 39.3% as moderate risk, and 3.1% as low risk. The developed comprehensive flood risk assessment model in current study can identify high-risk areas, prioritize mitigation measures, and aid in effective urban planning and disaster management.

Geographic-Information-System-Based Risk Assessment of Flooding in Changchun Urban Rail Transit System

The frequent occurrence of urban flooding in recent years has resulted in significant damage to ground-level infrastructure and poses a substantial threat to the metro system. As the central city’s core transportation network for public transit, this threat can have unpredictable consequences on travel convenience and public safety. Therefore, assessing the risk of urban flooding in the metro system is of utmost importance. This study is the first of its kind to employ comprehensive natural disaster risk assessment theory, establishing an assessment database with 22 indicators. We propose a GIS-based method combined with the analytical hierarchy process (AHP) and an improved entropy weight method to comprehensively evaluate the urban flood risk in Changchun City’s metro systems in China. This study includes a total of nine metro lines, including those that are currently operational as well as those that are in the planning and construction phases, situated in six urban areas of Changchun City. In this study, we utilize the regional risk level within the 500 m buffer zone of the metro lines to represent the flood risk of the metro system. The proposed method assesses the flood risk of Changchun’s rail transit system. The results reveal that over 30% of Changchun’s metro lines are located in high-risk flood areas, mainly concentrated in the densely populated and economically prosperous western part of the central city. To validate the risk assessment, we vectorized the inundation points and overlaid them with the regional flood risk assessment results, achieving a model accuracy of over 90%. As no large-scale flood events have occurred in the Changchun rail transit system, we employed receiver operating characteristic (ROC) curves to verify the accuracy of the flood risk assessment model, resulting in an accuracy rate of 91%. These findings indicate that the present study is highly reliable and can provide decision makers with a scientific basis for mitigating future flood disasters.

Assessment of Supply and Demand of Regional Flood Regulation Ecosystem Services and Zoning Management in Response to Flood Disasters: A Case Study of Fujian Delta

Global climate change has led to flood disasters increasing in terms of frequency and damage caused, which seriously threatens urban and rural security. The flood regulation (FR) service function of the ecosystem plays an important role in mitigating flood disaster risk. Previous studies on flood regulation ecosystem services (FRES) are still lacking in a cross-scale assessment of supply and demand, refined simulation of regional complex hydrology, and application of spatial zoning management. Taking the Fujian Delta as an example, this study established a cross-scale research framework based on the social-ecosystem principle. The SWAT model was used to simulate the regional hydrological runoff and calculate the macro-scale supply of FRES. Taking patches of land as units, a flood risk assessment model was constructed to calculate the micro-scale demand for FRES for urban and rural society. Through a comparison of supply and demand across spatial scales, a zoning management scheme to deal with flood disaster risk was proposed. The results showed that: (1) The supply of FRES differed greatly among the sub-basins, and the sub-basins with low supply were mostly distributed in the lower reaches of Jiulong River and the coastal areas. (2) The demand for FRES was concentrated in high-density urban built-up areas. (3) By comparing the supply and demand of FRES in sub-basin units, 2153 km2 ecological space was identified as the primary ecological protection area, and 914 km2 cultivated land and bare land were identified as the primary ecological restoration area. (4) By comparing the supply and demand of FRES of land patch units, 65.42 km2 of construction land was identified as the primary intervention area. This study provides a decision-making basis for regional flood disaster management from the perspective of FRES.

Performance comparison of two deep learning models for flood susceptibility map in Beira area, Mozambique

Floods are potentially devastating natural hazards that can threaten human life and ecosystems. The serious impact caused by floods is the reason for the urgent need to provide an appropriate flood susceptibility map (FSM). Floods can be triggered by various meteorological events, such as storms in coastal areas. On 14 March 2019, Cyclone Idai produced massive and severe flooding inundation in the coastal city of Beira, Mozambique. The extensive flood damage in Beira necessitated providing an accurate FSM and analyzing the factors associated with flooding. This study integrated the data of synthetic aperture radar (SAR), geographic information system (GIS), and two deep learning models, namely, the group method of data handling (GMDH) and convolutional neural network (CNN), to generate a precise FSM. We compared the performance of CNN and GMDH models in generating the FSMs for a case study of the March 2019 flood in the Beira Area, Mozambique. The result showed that the models produced a similar pattern of FSM, where the areas near the river, having a lower slope and lower altitude, have a higher flood risk. The FSM generated by CNN and GMDH models were compared to find a better model for flood risk assessment. The prediction performance of CNN model (AUC = 0.90 and RMSE = 0.022) was better than the result of GMDH model (AUC = 0.87 and RMSE = 0.089). The study should be used to mitigate the flood disaster and reduce flood risk in the future.

Human Settlement Resilience Zoning and Optimizing Strategies for River-Network Cities under Flood Risk Management Objectives: Taking Yueyang City as an Example

The dense river network and large population in the southern region of China are vulnerable to flooding stress, which challenges the construction of human settlements. This paper analyzes the causes of flood risk and the dilemma of human settlement improvement in river-network cities, introduces the principle of resilience to human settlements, and conducts a quantitative study on the resilience of human settlements in river-network cities with the goal of flood risk management. Taking Yueyang city, a river-network city in the Yangtze River Basin, as the empirical research object, we conduct a flood resilience zoning of the human settlements based on the flood risk assessment model and use the GIS spatial overlay method to correct the resilience of the current human living space against the territorial spatial planning of Yueyang city. Ultimately, we propose a strategy for optimizing human settlements under flood risk management. The results show that (1) the highest-risk and high-risk areas of Yueyang city were mainly located in Dongting Lake and its interconnected water system, the southwest local area and the southeastern fringe, while the low-risk and lowest-risk areas were mainly located in the northeast local area and the northwestern fringe, with low flood stress risk. (2) The spatial system of human settlement resilience was constructed based on the flood risk assessment level. Among them, the human settlement flood resilience zoning of Yueyang city was divided into five categories from low to high: human settlement control zone, restriction zone, buffer zone, construction zone and expansion zone; the flood resilience zoning of Yueyang city’s current living space was divided into three categories from low to high: structure adjustment zone, flood restriction zone and development stability zone. (3) The specific control implementation and execution of the human settlements in Yueyang city mainly focus on the interrelationship between the risk of flooding in the watershed and the development of human activities through zoning regulation and collaborative management to optimize the human settlements. The study results can provide positive intervention and guidance for constructing urban and rural territorial spatial prevention planning and improving human living quality in river-network cities in China.