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.

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.

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.

Integrating powerful machine learning models with flood risk assessment and determining the potential mechanism between risk and the driving factors are crucial for improving flood management. In this study, six machine learning models were utilized for flood risk assessment of the Pearl River Delta, in which the Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) models were firstly applied in this field. Twelve indices were chosen and 2000 sample points were created for model training and testing. Hyperparameter optimization of the models was conducted to ensure fair comparisons. Due to uncertainty in the sample dataset, recorded inundation hot-spots were utilized to validate the rationality of the flood risk zoning maps. After determining the optimal model, the driving factors of different flood risk levels were investigated. Urban and rural areas and coastal and inland areas were also compared to determine the flood risk mechanism in different highest-risk areas. The results showed that the GBDT performed best and provided the most reasonable flood risk result among the six models. A comparison of the driving factors at different risk levels indicated that the disaster-inducing factor, disaster-breeding environment, and disaster-bearing body were not definitely becoming more serious as the flood risk increased. In the highest-risk areas, rural areas were featured by worse disaster-breeding environment than urban areas, and the disaster-inducing factors of coastal areas were more serious than those of inland areas. Moreover, the Digital Elevation Model (DEM), maximum 1-day precipitation (M1DP), and road density (RD) were the top three significant driving factors and contributed 52% to flood risk. This study not only expands the application of machine learning and deep learning methods for flood risk assessment, but also deepens our understanding of the potential mechanism of flood risk and provides insights into better flood risk management.

Floods occur frequently and cause considerable damage to local environments. Effectively assessing the flood risk contributes to reducing loss caused by such disasters. In this study, the weighted naïve Bayes (WNB) method was selected to evaluate flood risk, and the entropy weight method was employed to compute the weights. A sampling and verifying model was employed to generate the most accurate conditional probability table (MACPT) to calculate the probability of flooding. When using the framework integrating WNB with the sampling and verifying model, previous studies could not obtain a WNB-based MACPT and the WNB classification accuracy, for lacking WNB functions that could be called directly. Facing this issue, in this study we developed WNB functions with the MATLAB platform to directly integrate with the sampling and verifying model to generate a WNB-based MACPT, contributing to the greater interpretability and extensibility of the model. Shantou and Jieyang cities in China were selected as the study area. The results demonstrate that: (1) a WNB-based MACPT can reflect the real spatial distribution of flood risk and (2) the WNB outperform the NB when integrated with the sampling and verifying model. The resulting gridded estimation reveal a detailed spatial pattern of flood risk, which can serve as a realistic reference for decision making related to floods. Furthermore, the proposed method uses less data, which would be helpful in developing countries where long-term intensive hydrologic monitoring is limited.

Due to the influence of buildings on the distribution of flood and their economic and social attributes, 3D spatial information such as the size of buildings and the flooded ratio of buildings relative to their height has an increasing impact on urban flood risk. However, existing flood risk assessment methods mainly use data in 2D and analysis methods are mostly 2D. In this study, flood variation processes were analyzed in the form of 3D dynamic visualization by coupling an urban drainage model and a flood simulation model with 3D visualization methods. By further combining with 3D building models, the 3D spatial information of buildings related to flood was obtained. In order to study the influence of 3D information on flood risk and combine with other multi-source heterogeneous data for integrated analysis, a 3D visualization assessment and analysis method for flood risk, coupled with the projection pursuit-particle swarm optimization algorithm (PP-PSO) was established (3DVAAM-PP-PSO). A case study from Chaohu City, China, was used to demonstrate the method. The results showed that the PP-PSO algorithm can process high-dimensional information and obtain the objective weight of each index. The 3D information from the influenced buildings had an impact on the evaluation results, which needed to be considered. Through the 3D visualization analysis, the overall distribution of flood risk and that around the buildings were obtained in multi-perspectives. The flood risk during different rainfall return periods were analyzed intuitively and comparatively. This study furnishes a novel method for flood risk assessment and analysis by making the most of 3D spatial information.

Metro system is a vital component of mass transportation infrastructure, providing crucial social and economic service in urban area. Flood events may cause functional disruptions to metro systems; therefore, a better understanding of their vulnerability would enhance their resilience. A comparative study of flood risk in metro systems is presented using the analytic hierarchy process (AHP) and the interval AHP (I-AHP) methods. The flood risk in the Guangzhou metro system is evaluated according to recorded data. Evaluated results are validated using the flood event occurred in Guangzhou on May 10, 2016 (hereinafter called \"May 10th event\"), which inundated several metro stations. The flood risk is assessed within a range of 500 m around the metro line. The results show that >50% of metro lines are highly exposed to flood risk, indicating that the Guangzhou metro system is vulnerable to flood events. Comparisons between results from AHP and I-AHP show that the latter yields a wider range of high flooding risk than the former.