Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches.

Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.

Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980.

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

Assessing social vulnerability and identifying spatial hotspots of flood risk to inform socially just flood management policy.

This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a national-scale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighborhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies.

Multiparameter flood hazard, socioeconomic vulnerability and flood risk assessment for densely populated coastal city.

In the current study, flood risk assessment of densely populated coastal urban Surat City, on the bank of the lower Tapi River in India, was conducted by combining the hydrodynamic model-based flood hazard and often neglected socioeconomic vulnerability. A two-dimensional (2D) hydrodynamic (HD) model was developed using physically surveyed topographic data and the existing land use land cover (LULC) of the study area (5248 km2). The satisfactory performance of the developed model was ascertained by comparing the observed and simulated water levels/depths across the river and floodplain. The 2D HD model outputs with geographic information system (GIS) applications were further used to develop probabilistic multiparameter flood hazard maps for coastal urban city. During a 100-year return period flood (Peak discharge = 34,459 m3/s), 86.5% of Surat City and its outskirt area was submerged, with 37% under the high hazard category. The north and west zones are the worst affected areas in Surat City. The socioeconomic sensitivity and adaptive capacity indicators were selected at the city's lowest administrative (ward) level. The socioeconomic vulnerability was evaluated by employing the robust data envelopment analysis (DEA) technique. Fifty-five of 89 wards in Surat City, covering 60% of the area under the jurisdiction of the Municipal Corporation, are highly vulnerable. Finally, the flood risk assessment of the city was conducted using a bivariate technique describing the distinctive contribution of flood hazard and socioeconomic vulnerability to risk. The wards adjoining the river and creek are at high flood risk, with an equal contribution of hazard and vulnerability. The ward-level hazard, vulnerability, and risk assessment of the city will help local and disaster management authorities to priorities high risk areas while planning flood management and mitigation strategies.

Locally Relevant High-Resolution Hydrodynamic Modeling of River Floods at the Regional Scale.

This paper deals with the simulation of inundated areas for a region of 84,000 km2 from estimated flood discharges at a resolution of 2 m. We develop a modeling framework that enables efficient parallel processing of the project region by splitting it into simulation tiles. For each simulation tile, the framework automatically calculates all input data and boundary conditions required for the hydraulic simulation on-the-fly. A novel method is proposed that ensures regionally consistent flood peak probabilities. Instead of simulating individual events, the framework simulates effective hydrographs consistent with the flood quantiles by adjusting streamflow at river nodes. The model accounts for local effects from buildings, culverts, levees, and retention basins. The two-dimensional full shallow water equations are solved by a second-order accurate scheme for all river reaches in Austria with catchment sizes over 10 km2, totaling 33,380 km. Using graphics processing units (GPUs), a single NVIDIA Titan RTX simulates a period of 3 days for a tile with 50 million wet cells in less than 3 days. We find good agreement between simulated and measured stage-discharge relationships at gauges. The simulated flood hazard maps also compare well with local high-quality flood maps, achieving critical success index scores of 0.6-0.79.

Flood Hazard Assessment and Mapping: A Case Study from Australia's Hawkesbury-Nepean Catchment.

Floods are among the costliest natural hazards, in Australia and globally. In this study, we used an indicator-based method to assess flood hazard risk in Australia's Hawkesbury-Nepean catchment (HNC). Australian flood risk assessments are typically spatially constrained through the common use of resource-intensive flood modelling. The large spatial scale of this study area is the primary element of novelty in this research. The indicators of maximum 3-day precipitation (M3DP), distance to river-elevation weighted (DREW), and soil moisture (SM) were used to create the final Flood Hazard Index (FHI). The 17-26 March 2021 flood event in the HNC was used as a case study. It was found that almost 85% of the HNC was classified by the FHI at 'severe' or 'extreme' level, illustrating the extremity of the studied event. The urbanised floodplain area in the central-east of the HNC had the highest FHI values. Conversely, regions along the western border of the catchment had the lowest flood hazard risk. The DREW indicator strongly correlated with the FHI. The M3DP indicator displayed strong trends of extreme rainfall totals increasing towards the eastern catchment border. The SM indicator was highly variable, but featured extreme values in conservation areas of the HNC. This study introduces a method of large-scale proxy flood hazard assessment that is novel in an Australian context. A proof-of-concept methodology of flood hazard assessment developed for the HNC is replicable and could be applied to other flood-prone areas elsewhere.

Remote Sensing Methods for Flood Prediction: A Review.

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country's economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology's practice and usage in flood prediction.

A Preliminary Contribution towards a Risk-Based Model for Flood Management Planning Using BIM: A Case Study of Lisbon.

Preparing a city for the impact of global warming is becoming of major importance. Adopting climate-proof policies and strategies in response to climate change has become a fundamental element for city planning. To this end, this research considers a multidisciplinary approach, at the local scale, able to connect urban planning and architecture, as a vital base for considering a coastal cities' ability to control the consequences of climate change, specifically floods. So far, there is a scarcity of research connecting sea ground and land surveys, and this study could become a foundational reference for coastline settlement management using BIM. We found in BIM (Building Information Modeling) a possible tool for managing coastal risk, since it can combine crowdsourced data for geometric and information modeling of the city. The proposed BIM model includes a topography used for 3D thematic maps, a riverbed model, and a waterway model. This model aims to facilitate coordination across separate actors and interests since the urban area model is always updatable and improvable. Focusing on a case study of Lisbon, we developed risk-based 3D maps of the area close to the shoreline of the Tagus River.

AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India.

Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approaches for alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R2 values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies.

Livelihood Vulnerability to Flood Hazard: Understanding from the Flood-prone Haor Ecosystem of Bangladesh.

Bangladesh is a country of natural disasters and climatic hazards, which frequently affect its inhabitants' lives and livelihoods. Among the various risks and disasters, floods are the most frequent hazard that makes haor households vulnerable. Therefore, this study was undertaken to estimate livelihood vulnerability to flooding within the flood-prone haor ecosystem in Bangladesh. Primary data were collected from 100 haor households each from Kishoreganj, Netrokona, and Sunamganj districts (N = 300) by applying a multistage random sampling technique. Data were collected through face-to-face interviews using a pretested structured questionnaire. The Livelihood Vulnerability Index (LVI) and the Intergovernmental Panel on Climate Change (IPCC) framework of vulnerability were applied to compare vulnerabilities among the selected haor-based communities. The empirical results revealed that haor households in Sunamganj district were more vulnerable to flood hazard and natural disaster in terms of food, water, and health than households in the other two districts. Taking into account the major components of the LVI, the IPCC framework of vulnerability indicated that households in Sunamganj district were the most vulnerable due to their lowest adaptive capacity and highest sensitivity and exposure. These findings enable policymakers to formulate and implement effective strategies and programs to minimize vulnerability and enhance resilience by improving the livelihoods of the vulnerable haor households of Bangladesh, especially those in Sunamganj district.

Climate change-induced variations in future extreme precipitation intensity-duration-frequency in flood-prone city of Adama, central Ethiopia.

The influences of climate change on the features of extreme rainfall events have become unprecedented that needs improved understanding at all levels for planning effective management strategies of the potential risks. This study aims to assess the potential influences of climate change on extreme rainfall characteristics in flood-vulnerable city of Adama. Daily precipitation records of 1967-2016 and projection of global circulation models (GCMs): CanESM2 and HadCM3 for 2021-2070 were disaggregated into shorter time resolutions using the Hyetos model. Gumbel type I probability distribution and power-regression model ([Formula: see text] were used for deducing intensity-duration-frequency (IDF) curves and for describing their functions, respectively. The extreme rainfall intensity of the historical and future periods for a range of storm durations and return periods were compared and contrasted. A close agreement is obtained between the observed and the modeled rainfall intensity with high values of coefficient of determination (> 0.996) and Nash-Sutcliffe efficiency (> 0.850). Besides, statistically significant (p < 0.05) direct linear relationship is found between the return periods and the coefficient parameter of the IDF models. Moreover, the intensity of extreme precipitation over 2021-2070 in Adama city would increase up to 49.5%, depending on storm duration and return period considered. This could have consequences of the way the city's drainage infrastructures are designed, operated, and sustained. Hence, flood-prone areas should be recognized in order to formulate effective strategies for mitigation and adaption of potential impacts. The standards for designing future drainage infrastructures should also be updated aiming to reflect the effects of climatic change.

Flood hazard delineation in an ungauged catchment by coupling hydrologic and hydraulic models with geospatial techniques-a case study of Koraiyar basin, Tiruchirappalli City, Tamil Nadu, India.

Flooding in urban basins is a major natural catastrophe that leads to many causalities of life and property. The semi-urbanized Koraiyar River basin in Tamil Nadu has important cities like Tiruchirappalli and many towns located in it. The basin unfailingly experiences a flood event in almost every decade. It is anticipated that the basin will undergo rapid unplanned urbanization in the years to come. Such fast and erratic urban developments will only increase the risk of urban floods ultimately resulting in loss of human lives and extensive damages to property and infrastructure. The effects of urbanization can be quantified in the form of land use land cover (LULC) changes. The LULC change and its impacts on urban runoff are studied for the continuous 30-year present time period of (1986-2016) to reliably predict the anticipated impact in the future time period of (2026-2036). The analysis of land cover patterns over the years shows that urbanization is more prevalent in the northern part of the basin of the chosen study area when compared with the other regions. The extreme rainfall events that occurred in the past, and the probable future LULC changes, as well as their influence on urban runoff, are studied together in the current study. In order to minimize flood damages due to these changing land use conditions, certain preventive and protective measures have to be adopted at the earliest. There are some inevitable limitations while applying traditional measures in flood modeling studies. This investigative work considers a case study on the ungauged Koraiyar floodplains. The spatial scale risk assessment is assessed by coupling geographic information systems, remote sensing, hydrologic, and hydraulic modeling, to estimate the flood hazard probabilities in the Koraiyar basin. The maximum flood flow is generated from the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS), the hydrologic model adopted in the present study. The maximum flood flow is given as input to the Hydrologic Engineering Centre-River Analysis System (HEC-RAS), an effective hydraulic model that generates water depth and flood spread area in the basin. The flood depth and hazard maps are derived for 2, 5, 10, 50, and 100-year return periods. From the analysis, it is observed that the minimum flood depth is less than 1.2 m to a maximum of 4.7 m for the 100-year return period of past to predicted future years. The simulated results show that the maximum flood depth of 4.7 m with flood hazard area of 4.32% is identified as high hazard zones from the years 1986-2036, located in the center of the basin in Tiruchirappalli city. The very high hazard flood-affected zone in the Koraiyar basin during this period is about 198.85 km2. It is noticed that the very low hazard zone occupies more area in the basin for the present and future simulations of flood hazard maps. The results show that the increase in peak runoff and runoff volume is marginally varied.

Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling.

This study deals with the use of remote sensing (RS), geographic information systems (GISs), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on the sustainable urban development of Tabuk City, Kingdom of Saudi Arabia (KSA). Determining the impact of flood hazards on the urban area and developing alternatives for protection and prevention measures were the main aims of this work. Tabuk City is exposed to frequent flash flooding due to its location along the outlets of five major wadis. These wadis frequently carry flash floods, seriously impacting the urban areas of the city. WMS and HEC-HMS models and RS data were used to determine the paths and morphological characteristics of the wadis, the hydrographic flow of different drainage basins, flow rates and volumes, and the expansion of agricultural and urban areas from 1998 to 2018. Finally, hydraulic modeling of the HEC-RAS program was applied to delineate the urban areas that could be inundated with floodwater. Ultimately, the most suitable remedial measures are proposed to protect the future sustainable urban development of Tabuk City from flood hazards. This approach is rarely used in the KSA. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban and agricultural development in the KSA.

Disparities of population exposed to flood hazards in the United States.

This study integrates publicly available datasets to provide a county-based assessment of socio-economic disparities of population exposure to flood hazards in the United States. Statistical analyses were applied to reveal the national trends and local deviations from the trends. Results show that approximately 21.8 million (6.87% of) U.S. population are exposed to 100-year-flood in 2015, and most of the exposure is near water bodies (e.g. ocean and rivers). Additionally, communities near water bodies are more responsive to potential flood hazards by avoiding residence in flood zones than inland communities. At the national scale, economically disadvantaged population are more likely to reside in flood zones than outside. At the local scale, economically disadvantaged population tend to reside in flood zones in inland areas, while coastal flood zones are more occupied by wealthier and elderly people. These findings point to an alarming situation of inland communities where people are generally less responsive to flood hazards and people in flood zones are in a lower economic condition. Using "hot spot" analysis, local clusters of disadvantaged population groups with high flood exposure were identified. Overall, this study provides important baseline information for policymaking at different levels of administration and pinpoints local areas where diversified and ad hoc strategies are needed to mitigate flood risk in communities with diverse socio-economic conditions. This study provides empirical evidence of socio-economic disparities and environmental injustice associated with flood exposure in the U.S. and offers valuable insights to the underlying factors.

Environmental injustice and flood risk: A conceptual model and case comparison of metropolitan Miami and Houston, USA.

This article outlines a conceptual model and comparatively applies it to results from environmental justice (EJ) studies of flood risk in the Miami, Florida, and Houston, Texas, metropolitan areas. In contrast to most EJ studies of air pollution, which have found that socially-vulnerable groups experience disproportionate risk, distributive EJ studies of flooding reveal inconsistent findings regarding the relationship between social vulnerability and flood exposure. Counterintuitively (from a conventional EJ perspective), some pre-flood EJ studies have found that socially-advantaged people experience the highest residential exposure to flood risks. To integrate those anomalous findings within an EJ perspective, our conceptual model focuses on (1) the differential capacities of social groups to deploy/access protective resources for reducing the threat of loss, even while they reside amid flood-prone environments, and (2) both flood hazards and water-based benefits. Application of this model in Miami reveals that environmental injustices materialize as socially-privileged groups expose themselves to residential flood risks by seeking coastal amenities, as the costs of mitigating risks are conveyed to the broader public; in the process, socially-vulnerable residents are relegated to areas with air pollution and/or inland flood risks, where they experience constrained access to protective resources and coastal amenities. Findings from Houston better align with conventional EJ expectations-with flood zones disproportionately inhabited by socially-vulnerable people-because many coastal lands there are used by petrochemical industries, which produce major residential-environmental disamenities. Results underscore the need to consider protective resources and locational benefits in future empirical research on the EJ implications of flood hazards.

Strong influence of El Niño Southern Oscillation on flood risk around the world.

El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss of life. However, although ENSO is known to influence hydrology in many regions of the world, little is known about its influence on the socioeconomic impacts of floods (i.e., flood risk). To address this, we developed a modeling framework to assess ENSO's influence on flood risk at the global scale, expressed in terms of affected population and gross domestic product and economic damages. We show that ENSO exerts strong and widespread influences on both flood hazard and risk. Reliable anomalies of flood risk exist during El Niño or La Niña years, or both, in basins spanning almost half (44%) of Earth's land surface. Our results show that climate variability, especially from ENSO, should be incorporated into disaster-risk analyses and policies. Because ENSO has some predictive skill with lead times of several seasons, the findings suggest the possibility to develop probabilistic flood-risk projections, which could be used for improved disaster planning. The findings are also relevant in the context of climate change. If the frequency and/or magnitude of ENSO events were to change in the future, this finding could imply changes in flood-risk variations across almost half of the world's terrestrial regions.

Conveying Flood Hazard Risk Through Spatial Modeling: A Case Study for Hurricane Sandy-Affected Communities in Northern New Jersey.

The accurate forecast from Hurricane Sandy sea surge was the result of integrating the most sophisticated environmental monitoring technology available. This stands in contrast to the limited information and technology that exists at the community level to translate these forecasts into flood hazard levels on the ground at scales that are meaningful to property owners. Appropriately scaled maps with high levels of certainty can be effectively used to convey exposure to flood hazard at the community level. This paper explores the most basic analysis and data required to generate a relatively accurate flood hazard map to convey inundation risk due to sea surge. A Boolean overlay analysis of four input layers: elevation and slope derived from LiDAR data and distances from streams and catch basins derived from aerial photography and field reconnaissance were used to create a spatial model that explained 55 % of the extent and depth of the flood during Hurricane Sandy. When a ponding layer was added to the previous model to account for depressions that would fill and spill over to nearby areas, the new model explained almost 70 % of the extent and depth of the flood. The study concludes that fairly accurate maps can be created with readily available information and that it is possible to infer a great deal about risk of inundation at the property level, from flood hazard maps. The study goes on to conclude that local communities are encouraged to prepare for disasters, but in reality because of the existing Federal emergency management framework there is very little incentive to do so.

Flood hazard and flood risk assessment using a time series of satellite images: a case study in Namibia.

In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood-prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov-Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat-5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.

A method for mapping flood hazard along roads.

A method was developed for estimating and mapping flood hazard probability along roads using road and catchment characteristics as physical catchment descriptors (PCDs). The method uses a Geographic Information System (GIS) to derive candidate PCDs and then identifies those PCDs that significantly predict road flooding using a statistical modelling approach. The method thus allows flood hazards to be estimated and also provides insights into the relative roles of landscape characteristics in determining road-related flood hazards. The method was applied to an area in western Sweden where severe road flooding had occurred during an intense rain event as a case study to demonstrate its utility. The results suggest that for this case study area three categories of PCDs are useful for prediction of critical spots prone to flooding along roads: i) topography, ii) soil type, and iii) land use. The main drivers among the PCDs considered were a topographical wetness index, road density in the catchment, soil properties in the catchment (mainly the amount of gravel substrate) and local channel slope at the site of a road-stream intersection. These can be proposed as strong indicators for predicting the flood probability in ungauged river basins in this region, but some care is needed in generalising the case study results other potential factors are also likely to influence the flood hazard probability. Overall, the method proposed represents a straightforward and consistent way to estimate flooding hazards to inform both the planning of future roadways and the maintenance of existing roadways.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India).

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.