Susceptibility To Flooding Research Articles

Flood risk assessment under the shared socioeconomic pathways: a case of electricity bulk supply points in Greater Accra, Ghana

This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055—more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana’s contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Data-driven urban configuration optimization: An XGBoost-based approach for mitigating flood susceptibility and enhancing economic contribution

The indiscriminate evolution of urban configurations aggravates flood vulnerabilities, threatening sustainable urban expansion. Present methodologies fall short in supplying urban planners with flood mitigative strategies centered on urban configuration facets. Leveraging the power of the XGBoost algorithm, this study posits an advanced optimization schema, adroitly balancing the dual objectives of mitigating urban flooding and enhancing economic growth, with minimal disruption to established urban layouts. Shenzhen serves as the investigative ground, where the model displays exceptional accuracy, resilience, and interpretability in predicting Pluvial Flooding Susceptibility (PFS) and Economic Contribution (EC). Model interpretation divulges the profound influence of three-dimensional urban configuration elements, primarily the Building Congestion Degree, on PFS and EC. Pareto solution exploration for multi-objective optimization unveils the ideal urban configuration interval. To minimize PFS while maximizing EC, the research suggests pertinent measures: augmenting vegetation density, regulating the impervious coverage ratio within 50–70%, limiting two- and three-dimensional building density thresholds, and moderately escalating urban drainage network density. Additionally, it encourages a comprehensive appreciation of function-oriented land usage and intrinsic site topographical characteristics to reconcile varied urban development goals during planning. By fusing data-derived insights with multi-objective optimization, this research anticipates influencing urban planning models, thus enhancing decision-making related to urban configuration and fostering flood-resilient, sustainable, and economically prosperous urban habitats.

Advancing Flood Risk Assessment through Integrated Hazard Mapping: A Google Earth Engine-Based Approach for Comprehensive Scientific Analysis and Decision Support

This study utilises a comprehensive, multi-layered approach to assess flooding susceptibility in a specific area, integrating diverse environmental datasets such as JRC Global Surface Water, Landsat 8 images, and SRTM elevation data. Employing the GEE FMA, a powerful tool leveraging Google Earth Engine capabilities, the analysis covers water occurrence, permanent water, elevation, distance to water, topographic hazard score, and vegetation indices (NDVI and NDWI). The Water Occurrence layer establishes a foundational understanding of water-body distribution’s correlation with flood vulnerability, while Permanent Water refines this understanding. Distance to Water measures proximity for targeted risk evaluation, and Elevation identifies vulnerable regions based on topography. The GEE FMA synthesises these layers into a Flood Hazard Susceptibility map, categorising vulnerability into Very Low, Low, Medium, High, and Very High. This nuanced understanding is crucial for prioritising interventions. The GEE FMA’s rapid processing speed makes it an invaluable tool for short-term decision support in flood hazard disaster management, offering insights for informed decision-making and resilient infrastructure development. The Topographic Hazard Score provides information on how topography influences flood risk, while the Wetness Hazard Score categorises moisture conditions for identifying flood-prone locations. Decision-makers rely on these values for quick and precise flood susceptibility assessments. In an era of climate uncertainties and urbanisation, the GEE FMA emerges as a reliable tool for decision-making, mitigating flood impacts, and developing effective flood risk management strategies.

NeuralFlood: An AI-Driven Flood Susceptibility Index

Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices’ models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.

Assessment of urban flood susceptibility and role of urban green space (UGS) on flooding susceptibility using GIS-based probabilistic models.

With rapid urbanization, the green space in urban areas is replaced with impervious built-up areas, which increases the frequency of urban floods. Kamrup Metropolitan District, Assam, is near the Brahmaputra and is highly prone to urban flooding. The present study aims to develop the urban flood susceptibility index (FSI) and to analyze the role of urban green space (UGS) as a nature-based solution (NBS) for urban flood susceptibility. Two types of flooded urban areas are observed using a two-stage cluster analysis. A GIS-based urban FSI is developed using logistic regression (LR), frequency ratio (FR), Shannon entropy (SE), certainty factor (CF), and weight of evidence (WoE) models, and variation of FSI is assessed for different UGS areas. According to the area under curve (AUC), the performance of all five models falls under the good to excellent class. The average UGS ratio for non-flooded is higher than for flooded areas, and with an increase in the area of UGS, the flooding probability decreases for all the models. The findings of the present study emphasize the importance of UGS and can be used for effective urban flood risk mitigation and management planning.

An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected by city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations affected urban flooding. This study, taking entered on Shenzhen City, deploys an XGBoost, integrating SHapley Additive exPlanation and Partial Dependency Plots, to assess how urban morphology influences urban flooding susceptibility. The models and strategies presented in this study aimed to adapt to extreme storms from the perspective of city spatial configuration planning. The findings underscore the varying impact of disaster variables on urban flooding, with morphological attributes becoming highly significant during severe inundations. In the analysis, mean building volume emerged as a pivotal parameter, with a mean SHAP value of 0.0107 m and a contribution ratio of 9.70 %. The study indicates that building configurations should be optimized to minimize urban flooding risks. It is recommended that the Mean Building Volume (MBV) be maintained within the range of 1.25 km3 to 2.5 km3, and the Standard Deviation of Building Volume (SDBV) be kept below 2.814 km3. By harnessing explainable Artificial Intelligence algorithms, this study offers insights into the intricate relationship between urban forms and flood risk, thereby informing the development of effective urban flooding adaptation strategies.

NeuralFlood: an AI-driven flood susceptibility index

Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices' models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.

Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways

Global climate change and rapid urbanization, mainly driven by anthropogenic activities, lead to urban flood vulnerability and uncertainty in sustainable stormwater management. This study projected the temporal and spatial variation in urban flood susceptibility during the period 2020–2050 on the basis of shared socioeconomic pathways (SSPs). A case study in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was conducted for verifying the feasibility and applicability of this approach. GBA is predicted to encounter the increase in extreme precipitation with high intensity and frequency, along with rapid expansion of constructed areas, resulting in exacerbating of urban flood susceptibility. The areas with medium and high flood susceptibility will be expected to increase continuously from 2020 to 2050, by 9.5 %, 12.0 %, and 14.4 % under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. In terms of the assessment of spatial-temporal flooding pattern, the areas with high flood susceptibility are overlapped with that in the populated urban center in GBA, surrounding the existing risk areas, which is consistent with the tendency of construction land expansion. The approach in the present study will provide comprehensive insights into the reliable and accurate assessment of urban flooding susceptibility in response to climate change and urbanization.

Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling

The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed.

Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of the new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fifteen climatic and geo-environmental variables were used as inputs into all flood susceptibility models: altitude, aspect, slope, plan curvature, profile curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing flood field survey and other information available for the analyzed area, a total of 118 flood locations were identified as potentially prone to flooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the location’s altitude and distance from the river are the most important variables for assessing flooding susceptibility.

A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer

Many areas around the world are affected by Groundwater Level rising (GWLr). One of the most severe consequences of this phenomenon is Groundwater Flooding (GF), with serious impacts for the human and natural environment. In Europe, GF has recently received specific attention with Directive 2007/60/EC, which requires Member States to map GF hazard and propose measures for risk mitigation.In this paper a methodology has been developed for Groundwater Flooding Susceptibility (GFS) assessment, using for the first time Spatial Distribution Models. These Machine Learning techniques connect occurrence data to predisposing factors (PFs) to estimate their distributions. The implemented methodology employs aquifer type, depth of piezometric level, thickness and hydraulic conductivity of unsaturated zone, drainage density and land-use as PFs, and a GF observations inventory as occurrences. The algorithms adopted to perform the analysis are Generalized Boosting Model, Artificial Neural Network and Maximum Entropy. Ensemble Models are carried out to reduce the uncertainty associated with each algorithm and increase its reliability. GFS is mapped by choosing the ensemble model with the best predictivity performance and dividing occurrence probability values into five classes, from very low to very high susceptibility, using Natural Breaks classification.The methodology has been tested and statistically validated in an area of 14,3 km2 located in the Metropolitan City of Naples (Italy), affected by GWLr since 1990 and GF in buildings and agricultural soils since 2007. The results of modeling show that about 93% of the inventoried points fall in the high and very high GFS classes, and piezometric level depth, thickness of unsaturated zone and drainage density are the most influencing PFs, in accordance with field observations and the triggering mechanism of GF. The outcomes provide a first step in the assessment of GF hazard and a decision support tool to local authorities for GF risk management.

Características hidrogeomorfométricas da microbacia Três Galhos, Amazônia Ocidental, Brasil

A aquisição de informações sobre as características da paisagem é essencial para o planejamento de uso e conservação dos recursos naturais. Em face ao exposto, o presente estudo tem como objetivo disponibilizar informações geométricas, topográficas e hidrográficas da microbacia do rio Três Galhos. Para a aquisição das informações foram utilizados softwares (QGIS, Google Earth e TrackMaker Free), imagens altimétricas (satélite ALOS) e equações, e para a discussão dos resultados foram utilizados dados da literatura. A microbacia do rio Três Galhos tem área de 23,24 km2, perímetro de 26,13 km, forma alongada, baixa suscetibilidade a inundações (ponto de vista geométrico), altitudes de 247 a 444 m, predominância do relevo suave ondulado (45,1%), rede de drenagem de 91,33 km, padrão dendrítico, 6ª ordem de drenagem (elevada condição para habitação de peixes), 12,62 nascentes km-2 (alta), densidade de drenagem de 3,93 km km-2 (muito alta), índice de sinuosidade de 39,94% (canal principal sinuoso), coeficiente de manutenção de 254,5 m2 m-1 e tempo de concentração de 2,28 h (baixo). Portanto, as informações obtidas da microbacia do rio Três Galhos, podem auxiliar no planejamento e gestão dos recursos naturais na região, através de instituições (públicas e privadas) e proprietários de imóveis rurais, possibilitando a manutenção ou melhoria da qualidade de vida das gerações atuais e futuras.

Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal–Wallis test and Kolmogorov–Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages.

Morphometric Analysis and Flash Floods Assessment of River Taraba Basin in Taraba State, Nigeria

Morphmetric analysis was conducted for River Taraba Catchments Area in Taraba with an emphasis on its hydrological response to storm events using geospatial techniques. The Study Basin comprises of nine small watersheds. The drainage network is dendritic. RTCA is a 7th order drainage basin, with an area of 15777 km2, having a perimeter of about 959km.The Lo value registered was 1.64 km .This denote gentle slope and stretch paths network on the watershed. The mean Rb varies between 3.0 and 5.14 for the catchment, indicating a transitional zone of geological structure with a less remarkable influence of structural disturbances. Value of drainage density indicates moderate runoff potentials. This depicts geological structures which do not distort the drainage pattern as such. The Basin has long Lb, of 242 km. This is an indication of low flooding susceptibility. The elongated the basin length, the little the effect on the extent to which the surface runoff manipulate the basin and been lowered by agents of denudation. Catchment characterizations of the study area and basin morphometry do not typify high flooding susceptibility. Environmental degradation through proliferation of haphazard and uncontrolled development within and around the flood plain seems the cause of incessant flooding in the catchments. Hazards occur when people place themselves in the path of geophysical and climatological agents. Governments should discourage influx to buffer and exploitation of the drainage basin in the study area.

Integrating Internet media into urban flooding susceptibility assessment: A case study in China

This study demonstrates the value of the public and of media observations (i.e. ‘citizen science’) for the modeling and understanding of the predisposing factors of urban flooding as a contribution to flooding hazard assessment. The 2016 Wuhan flood in the center of China utilized observations of non-scientists' from Internet media to capture the spatial distribution of urban flooding. Various sources in online news, blogs, bbs, and governmental websites were included. Independent flood-related factors such as rainfall, wetlands degradation, elevation, land use and land cover, curvature, slope, and normalized difference vegetation index were integrated into a logistic regression model to produce a susceptibility map. The feasibility and accuracy of this model was evaluated via comparison with official inundation maps and shortcomings of previous drainage system design. The accuracy assessment by receiver operating characteristics curve analysis estimated a success rate of 95.4%. This result shows that the data from mainstream media and public observations provide accurate and comparable information on flood occurrence at the urban scale. The assessment can be used to plan and design preventative drainage strategies for decision-makers. Finally, the limitations of Internet media as a new data resource for flooding risk assessment and future directions are discussed.

Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models

This work focuses on comparing results of flood susceptibility modelling in the part of Middle Ganga Plain, Ganga foreland basin. Following inclusivity rule, 12 major flood explanatory factors including a new one, geomorphology, have been utilized. Out of 1000 randomly generated flood-points from 2008 Landsat 5 TM image derived flood polygon, 70% have been utilized for the training purpose of Shannon’s entropy (SE) model and 30% for area under receiver operating characteristic (AUROC) method of validation of both, SE and frequency ratio (FR), models. Result from FR shows that the contributions of specific classes of different explanatory factors to flooding susceptibility vary whereas the SE model suggests that geomorphology is the most contributing factor. The AUROC curve for SE (0.90) was better than that for FR (0.85). Hence, the SE model predicts flood-susceptible areas more accurately than the multivariate statistical model FR in the study area.

Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia)

Flooding is considered as the most dangerous natural catastrophe in the world. This paper develops a spatial multi-criteria decision production prototype for the evaluation of flooding susceptibility for the city of Tunis. Eight criterions were considered in order to identify and assess the spatial distribution of hazardous zones and were integrated in the Geographical Information System (ArcGIS). The analytical hierarchy process (AHP) is a mathematical technique for flood hazard modeling and decision support based on the weight and rank of each flood factor in order to calculate Flood Hazard Index (FHI) to generate a flood susceptibility map. This was classified from very low to very high classes flood potential. Five classes are observed: 2.85% (very low), 7.5% (low), 17.95% (moderate), 20.63% (high), and 51.06% (very high). FHI was coupled with a sensitivity analysis to derive the Flood Hazard Index with sensitivity (FHIS). This index evaluates the effect of each criteria which allows for better assessment of the role of each parameter in flood susceptibility. The results of this study provide a basis to limit the impacts of flood and protect urban zone against inundation.

Comparison between Different Distributed Methods for Flood Susceptibility Mapping

Flooding is one of the main natural hazards in Northern Europe and several areas of the Northern Boreal Hemisphere, where during intense rainfall events, several river basins are affected by a fast water level rise that may cause severe damage to human lives and properties. For these reasons, the development of flood models to identify susceptible areas is essential for decision-makers. Geographic Information Systems (GIS) are currently accurate and valuable support tools for defining flood susceptibility maps at different spatial scales. In this study, the prediction accuracy of different GIS-based procedures in the identification of flooding susceptibility is tested and compared. These procedures include the frequency ratio, a combination of the frequency ratio and logistic regression, a combination of the frequency ratio and Shannon’s entropy index, and the statistical index. Ten conditioning parameters of flooding susceptibility are considered: elevation, slope, curvature, land use, Topographic Wetness Index, Stream Power Index, hydrogeology, stream distance, flow direction and average annual rainfall. The comparison analysis is carried out by applying these methods to the study area of Devon County in Southwest England. A total of 225 flood events are used to define the models. For model validation, 1000 randomly selected training and testing sub-datasets have been used in the definition of the receiver operating characteristic (ROC) curves. The results show that the procedure based on the statistical index provides the highest accuracy and reliability in flood susceptibility predictions.

Using relief patterns and quartile deviation for modeling of flood susceptibility maps: examples from Presidente Kennedy and Conceição do Castelo, Espírito Santo, Brazil

Flood modeling is one of the layers that compose the preliminary susceptibility map, which after the field investigation step is part of the Mass and Flood Movement Susceptibility Map. These maps are produced by the Geological Survey of Brazil, through the National Plan for Risk Management and Response to Natural Disasters (Programa Nacional de Gestao de Riscos e Respostas a Desastres Naturais, PNGRRDN). Initially, the flood modeling methodology consisted of applying the HAND model based on the following variables, hydrographic basin, and soil susceptibility. However, several inconsistencies were observed during fieldwork, especially regarding the model capacity to describe regions with specific hydrological regimes. A methodological improvement using other variables became necessary. Among the proposed variables, the relief susceptibility to floods yielded the most satisfactory results, especially since it could be applied to the entire national territory and was, therefore, introduced to replace the hydrographic basin susceptibility. Furthermore, the methodology used for defining the thresholds of the three flooding susceptibility classes (high, average and low) has also been modified by using the quartile deviation, which provides a less subjective class distribution. Using relief susceptibility and quartile deviation in flood modeling was tested in the Conceicao do Castelo and Presidente Kennedy municipalities (Espirito Santo, Brazil), where the morphological configuration covers a wide variety of environments, which is fundamental for the validation of the new variable. The results of the new model were satisfactory. The various types of plains continue to be well represented while a substantial improvement has been observed in the representation of flood-susceptible areas such as marine terraces and colluvium ramps.