Approach For Flood Risk Assessment Research Articles

Remotely Sensed Data, Morpho-Metric Analysis, and Integrated Method Approach for Flood Risk Assessment: Case Study of Wadi Al-Arish Landscape, Sinai, Egypt

Evaluating and predicting the occurrence and spatial remarks of climate and rainfall-related destructive hazards is a big challenge. Periodically, Sinai Peninsula is suffering from natural risks that enthuse researchers to provide the area more attention and scientific investigation. Extracted information from the morpho-metric indices aids in understanding the flood potentiality over various sizes of drainage catchments. In this work, the morpho-metric analysis has been used in order to model the relative signals of flood vulnerability of 16 catchments in northern Sinai. The geospatial technique has been applied to process the digital elevation models (DEMs) in order to produce different analysis maps. Basic geometries, in addition to several morpho-metric indices, were extracted and analyzed by investigating the digital elevation models. Three different effective methods were applied separately to build up three models of flood susceptibility behaviors. Finally, two flood susceptibility signals were defined: the integration method and accurate pixel level conditions models. The integrated method analysis indicates that the western half of the study landscape, including catchments (12, 13, and 14), presents high levels of flood susceptibility in addition to catchment 9 in the eastern half, whereas the other catchments were found to provide moderate levels. The integrated flood susceptibility final map overlaid one of the most effective topographic indices (topographic position index, TPI). The integrated results aided in understanding the link of the general catchments morphometry to the in situ topography for mapping the different flood susceptibility locations over the entire study landscape. Therefore, this can be used for investigating the surface-specific reduction strategy against the impacts of flood hazards in the proposed landscape.

Pluvial Flash Flood Hazard and Risk Mapping in Croatia: Case Study in the Gospić Catchment

Since the beginning of the 21st Century, Europe has been affected by destructive floods. European Union Member States have an obligation to develop flood hazard and flood risk maps as support to the Flood Risk Management Plan (FRMP). The main objective of this study is to propose a methodological framework for hazard and risk assessment of pluvial flash floods in Croatia at the catchment level, which can be integrated into the FRMP. Therefore, a methodology based on the source–pathway–consequence approach for flood risk assessment is presented, which complies with the EU Floods Directive. This integrated and comprehensive methodology is based on high-resolution open data available for EU Member States. Three scenarios are defined for a low, medium, and high probability, defined by design storms of different durations. The proposed methodology consists of flood hazard analysis, vulnerability assessment, and risk analysis. Pluvial flash flood hazards are analyzed using a 2D hydrologic–hydraulic model. The flood vulnerability assessment consists of a GIS analysis to identify receptors potentially at risk of flooding and an assessment of susceptibility to potential flood damage using depth–damage curves. Flood risk is assessed both qualitatively in terms of risk levels and quantitatively in terms of direct damages expressed in monetary terms. The developed methodology was applied and tested in a case study in the Gospić catchment in Croatia, which surrounds a small rural town frequently affected by pluvial flash floods.

Urban Flood Risk Assessment in Zhengzhou, China, Based on a D-Number-Improved Analytic Hierarchy Process and a Self-Organizing Map Algorithm

Flood risk assessment is an important tool for disaster warning and prevention. In this study, an integrated approach based on a D-number-improved analytic hierarchy process (D-AHP) and a self-organizing map (SOM) clustering algorithm are proposed for urban flooding risk assessment. The urban flood inundation model and geographic information system (GIS) technology were used to quantify the assessment indices of urban flood risk. The D-AHP approach was adopted to determine the weights of the indices, which effectively makes up for the shortcomings of the AHP in dealing with uncertain evaluation information (such as fuzzy and incomplete information). In addition, the SOM clustering algorithm was applied to determine the flood risk level. It is a data-driven approach that avoids the subjective determination of a flood risk classification threshold. The proposed approach for flood risk assessment was implemented in Zhengzhou, China. The flood risk was classified into five levels: highest risk, higher risk, medium risk, lower risk, and the lowest risk. The proportion of the highest risk areas was 9.86%; such areas were mainly distributed in the central and eastern parts of the Jinshui District, the eastern part of the Huiji District, and the northeastern part of the Guancheng District, where there were low terrain and serious waterlogging. The higher risk areas accounted for 24.26% of the study area, and were mainly distributed in the western and southern parts of the Jinshui District, the southern part of the Huiji District, the middle and eastern parts of the Zhongyuan District, the northeastern part of the Erqi District, and the northwestern part of the Guancheng District, which consisted of economically developed areas of dense population and buildings, matching well with historical flooding events. To verify the effectiveness of the proposed approach, traditional approaches for risk assessment were compared. The comparison indicated that the proposed approach is more reasonable and accurate than the traditional approaches. This study showed the potential of a novel approach to flood risk assessment. The results can provide a reference for urban flood management and disaster reduction in the study area.

Improved urban flood risk assessment based on spontaneous-triggered risk assessment conceptual model considering road environment

Urban flood risk assessment is an important tool for urban disaster management. The current research mainly considered area itself as the evaluation object. Urban road networks are extremely vulnerable to floods as a basis for all socio-economic activities in the city. However, most studies on road environmental risk assessment only took road itself as the evaluation object and did not consider potential regional system risks such as traffic jam caused by road inundation. Based on this, this study proposes a spontaneous-triggered risk assessment (STRA) conceptual model that comprehensively considers the spontaneous risk (SR) caused by the subregional hazard, socio-economic exposure, and triggered risk (TR) from the cross-subregional traffic jam or interruption caused by the road inundation in the evaluated subregion. Taking Haidian Island in Haikou as an example, six indices were selected for the SR assessment based on the combined weight-gray fuzzy comprehensive risk assessment method. The maximum mean inundation depth of road (RMD), maximum inundation duration of road (RID), road betweenness centrality (RBC), and number of road bus lines (RBL) were selected for the TR assessment. Total flood risk (FR) of the study area was obtained by combining the two. The results indicate that, compared with the traditional SR, the highest and higher risk subregions are more concentrated in the study area center, and the risk of the outer ring subregions is reduced overall after considering the TR. According to statistics, the FR level of 68 subregions is lower than the SR level, most of which are in the outer ring of the study area, and the FR level of 17 subregions is higher than the SR level, most of which are located around the intersection of Haidian Fifth Road and People Road. The study outcomes provide a novel approach for flood risk assessment.

Flood risk assessment in Ya'an, Sichuan, China based on the emergy theory

Abstract The emergy theory provides a new approach for flood risk assessment from an ecological perspective. By employing the emergy method, we used five indicators (rainfall runoff, medical workers and students per 10,000 people, social fixed assets investment, unit land GDP, and land-use types) from three dimensions (natural environment, population, and social economy) and the GIS technique to assess the potential impact and risk of a flood disaster on different regions in Ya'an City. Our findings revealed regional differences in the distribution of flood risks in Ya'an City: Lushan County and Yucheng District face the highest risks, followed by Tianquan County and Mingshan District, and Shimian County has the lowest risk. The index method was employed to analyze the regional differences. By training a back-propagation neural network with data on flood disasters in the study area, we produced a flood risk distribution map. We found that Mingshan District, Lushan County and Yucheng District have higher risks than other regions. The results largely agree with what we obtained using the emergy method. Our study shows that flood risk assessment based on the emergy theory can provide a scientific basis for flood control and disaster relief initiatives.

GIS-based multicriteria approach for flood risk assessment in Sigus city, east Algeria

GIS-based multicriteria approach for flood risk assessment in Sigus city, east Algeria

Flood risk assessment using deep learning integrated with multi-criteria decision analysis

In this paper, we proposed a novel approach for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The framework of the flood risk assessment involves three main elements: hazard, exposure, and vulnerability. For this purpose, one of the flood-prone areas of Vietnam, namely Quang Nam province was selected as the study area. Data of 847 past flood locations of this area was analyzed to generate training and testing datasets for the models. In this study, we have used one of the popular Deep Neural Networks (DNNs) algorithm for generation of flood susceptibility map while Analytic Hierarchy Process (AHP), which is a popular MCDA approach, was used to generate the hazard, exposure, and vulnerability maps. We have also used hybrid models namely BFPA and DFPA which are the ensembles of Bagging and Decorate with Forest by Penalizing Attributes algorithm for the comparison of performance with DNNs method. Various standard statistical indices including Receiver Operating Characteristic (ROC) curves were used for the performance evaluation and validation of the models. Results indicated that integration of DNNs and MCDA models is a promising approach for developing accurate flood risk assessment map of an area for the better flood hazard management.

Analyzing the spatiotemporal dynamics of flood risk and its driving factors in a coastal watershed of southeastern China

Rapid urbanization and climate change can cause more extensive flood risk, in the absence of urgent and efficient adaptation measures. As the occurrence of floods varies with time and space, comprehensive and dynamical assessment of the spatiotemporal variability of flood risk and understanding of its drivers is vital for flood risk management. In this study, we developed a spatial multi-criteria analysis (SMCA) framework for quantifying the spatiotemporal dynamics of flood risk through a case study in a coastal watershed of southeastern China from 1990 to 2015. A comprehensive framework for flood risk assessment was constructed from the hazard, exposure, sensitivity and adaptive capacity components with 23 indicators. The results showed that the highest risk happened in the stage of 2006–2010, while the lowest risk stage was 2011–2015, with higher flood risk in the downstream areas of Jiulong River watershed (JRW). The contribution of each indicator reflects the difference in temporal, spatial and quantity aspects. The top 5 driving factors for JRW included: peak discharge, maximum daily rainfall, age structure, wetland, and reservoir. The risk perception showed a continuous growing impact on flood risk. However, some indicators only showed obvious contributions in the specified area: for example, the built-up expansion in Zhangzhou city; the increase of dike length and the improvement of dike standard in Xinluo district; and the increase of government financial investment in Zhangping and Liancheng district. This study demonstrates the well-performance of our proposed novel approach for flood risk assessment. Our results and conclusions are also of significance for policymakers to understand and point out the deficiencies in the current actions of flood adaption, and consequently develop more targeted and spatially-specific strategies for flood adaptation, in the context of climate change and rapid urbanization.

A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification

Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset. [Formula: see text]-means based on silhouette criterion discovered top three representatives of the optimal numbers of clusters inherent in the flood dataset. Experts’ judgment favored four clusters, while Squared Euclidean distance was the best performing distance measure. SOM provided cluster visuals of the input attributes within the four different groups and transformed the dataset into a labeled one. A 5-layered Adaptive Neuro Fuzzy Inference System (ANFIS) driven by hybrid learning algorithm was applied to classify and predict FSL. ANFIS optimized by Genetic Algorithm (GA) produced root mean squared error (RMSE) of 0.323 and Error Standard Deviation of 0.408 while Particle Swarm Optimized ANFIS model produced 0.288 as the RMSE, depicting 11% improvement when compared with GA optimized model. The result shows significant improvement in the classification and prediction of flood risks using single ML tool.

Flood Hazard, Vulnerability and Risk Assessment for Different Land Use Classes Using a Flow Model

This study is concerned with flood risk that can be assessed by integrating GIS, hydraulic modelling and required field information. A critical point in flood risk assessment is that while flood hazard is the same for a given area in terms of intensity, the risk could be different depending on a set of conditions (flood vulnerability). Clearly, risk is a function of hazard and vulnerability. This study aims to introducing a new approach of assessing flood risk, which successfully addresses this above-mentioned critical issue. The flood risk was assessed from flood hazard and vulnerability indices. Two-dimensional flood flow simulation was performed with Delft3D model to compute floodplain inundation depths for hazard assessment. For the purpose of flood vulnerability assessment, elements at risk and flood damage functions were identified and assessed, respectively. Then, finally flood risk was assessed first by combining replacement values assessed for the elements and then using the depth–damage function. Applying this approach, the study finds that areas with different levels of flood risk do not always increase with the increase in return period of flood. However, inundated areas with different levels of flood depth always increase with the increase in return period of flood. The approach for flood risk assessment adopted in this study successfully addresses the critical point in flood risk study, where flood risk can be varied even after there is no change in flood hazard intensity.

Flood risk assessment and management in urban areas: a case study in Bechar City, Southwestern Algeria

Flood has always been considered as one of the natural catastrophes that accompanies with huge losses and damages, which affect the lives of human beings. The present paper presents a flood risk assessment approach for Bechar city, an urban area in Southwest of Algeria using hydrologic engineering centres-river analysis system (HEC-RAS). In order to achieve better floodplain management in the study area, a suite of solutions were developed, taking into account hydraulic, infrastructure, environmental, geomorphic, economic and social considerations. Some of the key solutions identified included: 1) redevelopment of the Bechar Wadi over the entire urbanised area, on a width of the bed of the Wadi whose sizing will be ensured by a detailed study; 2) evacuation of excess flow through a bypass channel to Tigheline Wadi; 3) establishment of a dam in downstream of El Abiad Wadi. The results of this study will be of great help to the decision makers in the field of floodplain management, especially the involved government's department, to make a proper plan for future development.

Flood risk mapping for direct damage to residential buildings in Quebec, Canada

This paper presents a probabilistic approach for flood risk assessment in a reach of the Richelieu River, south of Saint-Jean-Sur-Richelieu, Quebec, Canada. The approach is based on a combination of three simple modules: 1) flood frequency analysis (frequency and peak discharge), 2) estimation of inundation depth, and 3) damage and loss estimation. To assess the flood negative impacts, a simple hydraulic model is developed designed to replace and complement the existing 2D model coupled to an existing damage model. By simplifying the spatial coverage of flood calculations, this approach accelerates the computational efficiency enabling a broader set of elements to be combined in a large sample of Monte Carlo simulations applied in the flood risk assessment. The final result is a local scale risk map indicating the expected annual damage to each individual building in the study area useful for flood risk decision making. The analyses show that the approach is particularly powerful for flood risk assessments in areas adjacent to the river for which sufficient data is available.

Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China

Floods are considered as one of the most frequently occurring natural hazards worldwide and are occurring increasingly frequent in recent decades. Flood risk assessment is an important tool for flood prevention and involves significant practical applications in flood risk management and flood disaster reduction. In the study, an integrated methodology is proposed by incorporating urban flood inundation model, improved entropy weight method and k-means cluster algorithm to evaluate urban flood risk. The proposed approach is data driven without considering classification standard of different risk levels, and thus provides a more reasonable and objective result. A region in Haikou, China is adopted to test the applicability of the proposed approach. Seven evaluation indices are selected by coupling the natural hazard index system and hydrological models. The index weights are calculated by an improved entropy weight method that integrates the entropy weight method and analytic hierarchy process (AHP) method. Subsequently, the k-means cluster algorithm is used to develop the flood risk map in the study area. The results indicate that high risk zones cover 13.7% of total area, which generally exhibit higher inundation depth and lower elevations. The assessment result matches well with the historical data of flood events. The traditional cluster algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) methods are used for comparison with the improved entropy-cluster algorithm so as to validate the proposed approach for risk management. The result demonstrates that the proposed approach is feasible and exhibits the most reasonable classification result. The study outcomes provide a novel approach for flood risk assessment and can provide valuable information for urban flood management.

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

ABSTRACTIn this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.

A GIS-supported fuzzy-set approach for flood risk assessment

This study presents a geographic information system (GIS)-supported three-dimensional fuzzy risk assessment approach (3D GIS-FRA) for flood risk assessment that is based on the development of a fuzzy-set risk model, 3D GIS mapping, and a hydro-statistical simulation. Using GIS and a digital elevation model (DEM), urban settings under different levels of flood risk are visualized and hydraulic simulations conducted for various river flow scenarios to determine the flow rates for specified risk levels. Then, a statistical analysis is carried out using historical records to establish a set of risk criteria that consider critical factors that affect the peak flow rate. Finally, the developed fuzzy-set risk model is applied to examine the flood risks using the outputs from the hydraulic models and statistical analysis. The developed method is applied to a section of the Red River in Southern Manitoba, Canada. The 3D GIS-FRA results indicate that there is a possibility of having a highly risky situation for the upper-bound extreme condition for the study area, although only limited impacts are expected for a 25-year flood. For the 75-year flood scenario, the overall flood risk level is high for the whole area. The results indicate that the developed risk analysis system is useful for systematically quantifying the flood risks and the related system uncertainties.

A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China

Floods, the most common natural hazard in the world, cause serious loss in terms of lives, buildings, and infrastructures. As a consequence, the need for flood risk assessment has become critical. Using a semi-quantitative model and fuzzy analytic hierarchy process (FAHP) weighting approach, this paper assessed flood risk in the Dongting Lake region, Hunan Province, Central China, an area where flood hazards frequently occur. The model was designed using spatial multi-criteria analysis (SMCA) techniques in a Geographic Information System (GIS). A GIS database of indicators for the evaluation of hazard and vulnerability was created. Each indicator was analyzed, standardized, and weighted; after which, the weights of the indicators were combined to obtain the final flood risk index map. Using the flood risk index, the study area was classified into five categories of flood risk: very low, low, medium, high, and very high. The high and very high risk zones are mainly concentrated in the northern and central plains. The results obtained can provide useful information for decision makers and insurance companies.

A GIS-supported 3D approach for flood risk assessment of the Qu'Appelle River, Southern Saskatchewan

This research introduces a hybrid approach for flood risk assessment through GIS. The developed approach is based on the integration of hydraulic simulation and GIS analysis, which allows spatial-based visualisation and prediction of flood disaster. In particular, the Hydraulic Engineering Center River Analysis System (HEC-RAS) model is employed to simulate multi-scenario flood cases within a Canadian context; the dimensional stream channel model is constructed based on geometry obtained from Digital Elevation Model (DEM), and ArcView GIS software is used for producing water surface grids, and Triangular Irregular Network (TIN) model is utilised for the study area based on Digital Terrain Model (DTM). The developed approach is applied to a section of the Qu'Appelle River in southern Saskatchewan. The results indicated that the developed methodology is efficient in modelling and visualising the spatial extent of different flood scenarios and in determining flooded areas at risk. It was found that changes in water elevations of the Qu'Appelle River made the downstream areas more at risk.