Risk assessment and adaptation have become key focuses in the examination of urban flooding risk. In recent decades, global climate change has resulted in a high incidence of extreme weather events, notably flooding. This study introduces a spatial multi-indicator model developed for assessing flood risk at the urban agglomeration scale. A crucial addition to the model is the incorporation of an adaptive capacity within the IPCC risk framework. The model systematically considers various flood risk indicators related to the economic, social, and geographic environments of the central and southern Liaoning urban agglomeration (CSLN). It generates a spatial distribution map of integrated flood risk for multiple scenario combinations. Furthermore, the intricate relationship between different risk indicators and flood risk was analyzed using correlation analysis and the Light Gradient Boosting Machine model (Light GBM). The findings reveal notable variations in flood risk under different scenarios. The inclusion of vulnerability indicators increased flood risk by 33 %, while the subsequent inclusion of adaptive indicators decreased flood risk by 45 %. Dense populations and assets contribute to high flood risk, while adaptive capacity significantly mitigates urban flood risk. The framework adopted in this paper can be applied to other areas where urban agglomeration-scale flood risk assessment is needed, and can contribute to advancing scientific research on flood forecasting and mitigation.

With climate change and urbanization, flood disasters have significantly affected urban development worldwide. In this study, we developed a paradigm to assess flood economic vulnerability and risk at the urban mesoscale, focusing on urban land use. A hydrological simulation was used to evaluate flood hazards through inundation analyses, and a hazard-vulnerability matrix was applied to assess flood risk, enhancing the economic vulnerability assessment by quantifying the differing economic value and flood losses associated with different land types. The case study of Wangchengpo, Changsha, China, found average total economic losses of 126.94 USD/m2, with the highest risk in the settlement core. Residential areas had the highest flood hazard, vulnerability, and losses (61.10% of the total loss); transportation areas accounted for 27.87% of the total economic losses due to their high flooding depth. Despite low inundation, industrial land showed greater economic vulnerability due to higher overall economic value (10.52% of the total). Our findings highlight the influence of land types and industry differences on flood vulnerability and the effectiveness of land-use inclusion in urban-mesoscale analyses of spatial flood characteristics. We identify critical areas with hazard and economic vulnerability for urban land and disaster prevention management and planning, helping to offer targeted flood control strategies to enhance urban resilience.

Flooding is a global threat and predicting flood risk accurately is vital for effective mitigation and increasing society\'s awareness of the negative impacts of floods. Over the years, researchers have worked on physical and data-driven models to predict flood damage, striving to improve accuracy and understanding. However, the challenge lies in the scarcity and limitedness of comprehensive datasets needed to develop these models. This study aims to enhance the National Flood Insurance Program (NFIP) claims dataset from Hurricane Katrina in coastal Alabama to make it adequate for multi-variable flood damage assessment. The NFIP claims dataset was combined with the Alabama property dataset, simulated flood hazard information, and property location characteristics. Oversampling techniques are employed to address data imbalance in the datasets. Subsequently, several ensemble machine learning approaches, including random forest, extra tree, extreme gradient boosting, and categorical boosting, are utilized to develop multi-variable flood damage models. The validation of these models demonstrates that extreme gradient boosting performs best, achieving satisfactory results in identifying damaged properties with precision (0.89), recall (0.90), and F1-score (0.90), as well as determining relative damage with R-squared (0.59), root mean squared error (0.21), and Spearman correlation (0.70). Utilizing data oversampling techniques improves the model performance of imbalanced flood damage datasets. Despite the dataset\'s limitations and data augmentation techniques employed, the model\'s output explanation based on SHapley Additive exPlanations (SHAP) is constructive as it aligns with the study\'s expectations regarding the interaction of different features to produce the final results.

As one of the most destructive nature hazards, hurricane-induced flooding generates serious adverse impacts on populations, infrastructure, and the environment globally. In urban areas, complex characteristics such as high population and infrastructure densities increase flood disaster risks. Consequently, the assessment of flood risks is becoming increasingly important for understanding potential impacts on an urban area and proposing disaster risk mitigation strategies. After conducting a comprehensive literature review, this study finds that most urban flood risk assessments often overlook urban ecosystem elements, focusing more on social and economic aspects. Hence, the role of urban ecosystems cannot be fully understood. To address this gap, this study proposes a social-ecological systems (SES) flood risk assessment framework for urban areas. Based on this framework, a comprehensive list of indicators collected through a literature review is provided for urban flood risk assessments. A comparative study of flood risk during Hurricane Harvey (2017) in Houston, Texas, USA, is carried out using the improved analytic hierarchy process (IAHP) weighting method and the equal weighting method for indicator weighting. Results are then compared with the damage data of Hurricane Harvey published by the U.S. Federal Emergency Management Agency (FEMA). The analysis identifies that the western part of Houston had the highest flood risks, while the center of Houston was at lower flood risk. Comparisons between the results from the IAHP and equal weighting methods show that the latter produces a broader range of high flood risk areas than the former. This study also highlights the role of urban ecosystems in mitigating flood risks and advocates for more holistic, social-ecological assessments of flood risk. Such assessments could utilize the proposed framework and the indicator list but contextualize these to the specific urban area\'s contexts being investigated.

Floods are one of the most frequent global natural hazards resulting in significant human and economic losses. Therefore, assessing and mapping flood hazard levels is essential to reduce the severity of future flood disasters. This study developed an integrated methodology to evaluate flood risk using an improved Analytic Hierarchy Process (AHP) and Entropy Weight (AHP-EW) method based on cosine similarity (COS-AHP-EW). This method has more scientific results because it combines subjective and objective information. The proposed method\'s viability was then tested in Wuhan, China. Fourteen flood-inducing indicators were identified for the flood hazard, vulnerability, and restorability index system, with the indicator weights calculated using the COS-AHP-EW. This study utilized the Jenks method to develop the Wuhan flood risk map. We observed that the very high risk and high-risk areas covered 2.43% and 11.54% of the total study area and were mainly distributed in the highest economic and urbanization development and low-permeability districts, respectively. The validation with the historical waterlogging points reflected the accuracy and reliability of the COS-AHP-EW. The superiority of the proposed method was further verified by comparing it with single-evaluation methods (AHP and Entropy Weight) and another combined weight method (combined AHP-EW based on ideal point theory, namely, Ideal-AHP-EW). The comparison results indicated that the COS-AHP-EW was more accurate at predicting the risk in flood-prone area. Flood risk maps generated using the COS-AHP-EW could be applied to improve flood risk assessments, and the proposed method could be extended to other study areas to provide reliable flood management information.

Nagaon is one of the highly flood-prone districts of Assam, India that recurrently experiences devastating floods resulting in the loss of lives and property and wreaking havoc on the district\'s socioeconomic infrastructure. Identification and mapping of spatial patterns of flood hazards, flood vulnerability, and flood risk zones (FRZs) of the district are, therefore, crucial for flood management and mitigation. The present study, therefore, attempts to delineate the FRZs of more than 930 villages in the Nagaon district by integrating the flood hazard and vulnerability layers in the geospatial environment using the multi-criteria decision analysis and analytical hierarchy process techniques. Here, seven flood hazard and vulnerability indicators are considered to derive each layer separately. The results indicate that about 15.14% of the district\'s total villages are in the very high FRZ, 27.93% in the high, 46.62% in the moderate, and 10.3% in the low FRZ. Further, bivariate correlation analysis is used to evaluate the results with the percentages of the population, cropland, and animals affected by floods at different temporal scales in order to ensure that the revenue circles with a higher percentage of area under high and very high FRZs genuinely have higher percentages of flood-affected cropland, people, and livestock. The significance of this research is evident in its pragmatic findings that could aid the stakeholders in managing and reducing flood risk at micro-spatial scales.

The State of Kerala has frequently been facing a series of flooding phenomena that have adversely affected its multiple sectoral growths. The floods of 2018 have happened to be one of the most devastating floods that have occurred in the State of Kerala. It was seen that nearly thirteen out of fourteen districts in Kerala were tremendously affected during the 2018 August floods. The worst affected districts during the 2018 floods were Trivandrum, Pathanamthitta, Idukki, Thrissur, Ernakulam, and Kottayam. A sub-region near the Karamana basin located in the Trivandrum district is considered for the present study. The Karamana sub-region is a highly urbanized area that is also more or less prone to intense riverine flooding. The major rivers-Karamana and Killi-along with their respective tributaries, are the water bodies in the study region. Extensive urbanization, along with the overflowing of rivers during monsoon seasons, has paved the way for intense flooding in the region. This, in turn, necessitates developing a flood model for the sub-region. The development of an efficient flood model will aid in understanding the future challenges related to a flooding event in a region. In this study, the flood return probability water levels for the 5-year, 10-year, 25-year, 50-year, 100-year, 250-year, and 500-year were estimated for the Karamana sub-region. Besides, the flood risk zoning for the study area was conducted and elaborated as very high risk, high risk, moderate risk, and low risk for the different areas of the sub-region. Overall, the study can be helpful in identifying the most vulnerable areas to flooding in the Karamana region. By the proper identification of vulnerable areas in the region, proper planning and early warning measures can be devised and carried out by policymakers.

Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen\'s kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district\'s relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.

Agricultural lands are often impacted by flooding, which results in economic losses and causes food insecurity across the world. Due to the world\'s growing population, land-use alteration is frequently practiced meeting global demand. However, land-use changes combined with climate change have resulted in extreme hydrological changes (i.e., flooding and drought) in many areas. The state of Iowa has experienced several flooding events over the last couple of decades (e.g., 1993, 2008, 2014, 2016, 2019). Also, agribusiness is conducted across 85% of the state. In this research, we present a comprehensive assessment for agricultural flood risk in the state of Iowa utilizing most up-to-date flood inundation maps and crop layer raster datasets. The study analyzes the seasonal variation of the statewide agricultural flood risk by focusing on corn, soybean, and alfalfa crops. The results show that over $230 million average annualized losses estimated at statewide considering studied crop types. The crop frequency layers and corn suitability rating datasets are investigated to reveal regions with lower or higher productivity ratings. The study founds nearly half a million acres of cropland is under 2-year return period flood zone. Additionally, a data-driven flood model, Height Above the Nearest Drainage (HAND), is used to analyze performance against the FEMA maps. We found that the HAND flood maps performed with the correlation of 0.93 and 0.94 for 100-year and 500-year flood events regarding to the FEMA maps.

In order to accurately simulate the whole urban flooding processes and assess the flood risks to people and vehicles in floodwaters, a 2D-surface and a 1D-sewer integrated hydrodynamic model was proposed in this study, with the module of flood risk assessment of people and vehicles being included. The proposed model was firstly validated by a dual-drainage laboratory experiment on the flood inundation process over a typical urban street, and the relative importance of model parameters and model uncertainties were evaluated using the GSA-GLUE method. Then the model was applied to simulate an actual urban flooding process that occurred in Glasgow, UK, with the influence of the sewer drainage system on flood inundation processes and hazard degree distributions of people and vehicles being comprehensively discussed. The following conclusions are drawn from this study: (i) The proposed model has a high degree of accuracy with the NSE values of key hydraulic variables greater than 0.8 and the GSA indicates that Manning roughness coefficients for surface and sewer flows, inlet weir and orifice discharge coefficients, are the most relevant parameters to influence the simulated results; (ii) vehicles are vulnerable to larger water depths while human stability is significantly influenced by higher flow velocities, with the overall flood risk of people being less than that of vehicles; and (iii) about 88.7% of the total inflow volume was drained to the sewer network, and the sewer drainage system greatly reduced the flood risks to people and vehicles except the local areas with large inundation water depths, where the sewer drainage increased the local flow velocity leading to higher flood risks especially for people.