Urban Flood Research Articles

Efficiency evaluation of low impact development practices on urban flood risk

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2–22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

Characteristics of Urban Flood Resilience Evolution and Analysis of Influencing Factors: A Case Study of Yingtan City, China

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological, and infrastructural. A hybrid model combining Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) From 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR were highly correlated with urban infrastructure development; however, the ecological resilience in the subsystem showed a fluctuating downward trend because of the influence of the surface area of lakes and rivers; (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction.

Spatiotemporal assessment of urban flooding hazard using social media: A case study of Zhengzhou ‘7·20’

Urban flooding is occurring more frequently and with more serious consequences. The importance of flood disaster management has been highlighted. Social media has become an important source of flood disaster information in flood hazard assessment, however, it suffers from a lack of geolocation information and many qualitative descriptions. To improve the application of social media in flood hazard assessment, this study proposed a flooding hazard assessment method. Machine learning (ML)-based named entity recognition (NER) technique was used to identify the location and time of inundation in tweets, which can complement and correct the spatiotemporal properties of tweets. The textual description of waterlogging level in social media was quantified by lexicon-based NER technique. Taking a heavy rainfall event that occurred in Zhengzhou in July 2021 as an example, the results show that social media-based flood hazard maps are effective in identifying waterlogging. This study provides a feasible way to obtain near real-time hazard information from social media to help rapid hazard response.

The urban environmental system perspective on socio-environmental risks of urban flooding

In the context of urban socio-environmental issues, floods stand out due to the amplitude of their impacts on building spaces and populations' lives. This problem has become an object of study in several knowledge areas, particularly from an interdisciplinary perspective, for understanding and identifying risks and socio-environmental vulnerabilities associated with hydrological disasters. The occurrence of extreme precipitation events recorded in Brazilian cities has also drawn attention of both public managers and agents, since the floods of urban rivers have caused, each year, serious damage to the population and services located in risk areas. Overall, floods in large, medium-sized, and even small cities are related to land use and land cover, as well as to rainwater management in urban spaces. Driven by these issues, this study presents results regarding the socio-environmental risks of urban flooding in Francisco Beltrão, a town located in Paraná State-Southern Brazil. It is based on the Urban Environmental System (UES) methodology, which is applied to identify social and environmental factors that intervene in the production of risks and vulnerabilities related to hydrological disasters. In the social subsystem domain, material losses related to urban flooding in Francisco Beltrão evidence the vulnerabilities of the affected population and the economic and social damage. On the other hand, regarding the natural subsystem domain, there is an increased hazard of extreme hydrometeorological events, which might lead to river flooding. Considering the urban socio-environmental system of Francisco Beltrão, the population's exposure to the flood hazard and the environmental degradation of urban rivers points to the use and occupation of risk areas.

A decentralized multi-agent framework for urban flood management

The management of water resources in urban environments is challenging due to the existence of numerous stakeholders and the large number of affected people. In this paper, agents in an urban environment are categorized as active, passive, and monitoring agents, and a new mechanism is proposed to determine the flood control motivation functions (FCMFs) of active agents. An FCMF shows the motivation of an agent for investing and participating in a flood control activity. Also, for the first time, the effectiveness of a conflict resolution model based on asynchronous distributed constraint optimization (ADOPT) is evaluated for urban flood management. To evaluate the effectiveness of the proposed framework, it is applied to the eastern drainage catchment of Tehran, the capital city of Iran. The FCMFs of active stakeholders are modeled using some mathematical functions. To determine the FCMFs of active agents, an economic optimum point is suggested. This point is determined based on the construction costs of flood control infrastructures and flood damage costs. The utility functions of passive agents are modeled using a regression analysis. Flood management scenarios include a combination of water transfer canals, pipes, and ponds with real-time operating methods. ADOPT is used for solving Distributed Constraint Optimization Problems (DCOPs). The goal in DCOPs is to assign values to these variables in an approach that maximizes a global objective function while satisfying the constraints of each agent. The results show that by using the ADOPT-based conflict resolution model, the average depth of flooding is reduced from 24 cm to about 10 cm, and this is associated with the satisfaction of more than 90 percent of stakeholders. A decline in Average Flood Depth (AFD) has been observed with increased usage of technological methods such as constructing ponds or transferring water outside the basin. However, stakeholder desirability is critical and both technical and stakeholder interests should be taken into account to ensure project viability.

Enhancing solids deposit prediction in gully pots with explainable hybrid models: A review.

Urban flooding has made it necessary to gain a better understanding of how well gully pots perform when overwhelmed by solids deposition due to various climatic and anthropogenic variables. This study investigates solids deposition in gully pots through the review of eight models, comprising four deterministic models, two hybrid models, a statistical model, and a conceptual model, representing a wide spectrum of solid depositional processes. Traditional models understand and manage the impact of climatic and anthropogenic variables on solid deposition but they are prone to uncertainties due to inadequate handling of complex and non-linear variables, restricted applicability, inflexibility and data bias. Hybrid models which integrate traditional models with data-driven approaches have proved to improve predictions and guarantee the development of uncertainty-proof models. Despite their effectiveness, hybrid models lack explainability. Hence, this study presents the significance of eXplainable Artificial Intelligence (XAI) tools in addressing the challenges associated with hybrid models. Finally, crossovers between various models and a representative workflow for the approach to solids deposition modelling in gully pots is suggested. The paper concludes that the application of explainable hybrid modeling can serve as a valuable tool for gully pot management as it can address key limitations present in existing models.

Minimizing Urban Flooding by Optimal Design of Drainage System Using Sequential Least Squares Quadratic Programming and Spatial Datasets

Urban flooding is caused due to poor drainage design and excessive rain. It severely affects the road infrastructure. Existing hydrologic software tools to examine the extent of urban flooding primarily require walking through a series of manual steps and address each study area individually, preventing a collective review of poor storm-drains in an efficient manner. Previous methods for optimal drainage design were inefficient and lacked the ability of solving the underlying optimization problem due to the inherent nonlinearity of the decision variables. In this paper, we develop a nonlinear optimization formulation to minimize urban flooding using underground pipe size as a decision variable. We propose a solution algorithm using sequential least squares quadratic programming and spatial datasets. The proposed method eliminates the need to examine each study area manually using existing hydrologic tools. An example using the storm-drain system for the Baltimore County is performed. The results show that the model is effective in identifying storm-drain deficiencies and correcting them by choosing appropriate storm-drain inlet types to minimize flooding. Future works may include using large datasets and a more sophisticated modeling approach for estimating rainfall intensity based on extreme weather patterns. The method can be applied to other jurisdictions if relevant hydrological and underdrain piping network data were available.

Urban pluvial flood modelling in the absence of sewer drainage network data: A physics-based approach

1D/2D dual drainage models have become one of the most useful tools in the study of urban pluvial floods. However, such models require information about the sewer network that is not always readily available. In this study we present a physics-based approach for assessing urban pluvial floods, with the aim of overcoming the common situation of having limited information on the sewer system. The method proposed involves the use of available open-access information within a virtual sewer network generation tool. This realistic approach allows for the implementation of 1D/2D dual drainage models, such as the recently developed Iber-SWMM model. Results obtained from four storm events using a virtual sewer network in Iber-SWMM were compared with those derived using data from the actual sewer network in a coastal town located in NW Spain. These results revealed that the proposed approach can reasonably represent the sewer network's drainage capacity during pluvial floods, especially compared to other simplified approaches, like the rainfall reduction method. The proposed approach also accounts for the sewer network transport capacity and the impact of overflows when a sewer’s capacity is exceeded. Hence the study confirms the significant effect of these processes on the magnitude of pluvial flooding in urban areas. The methodology is shown to be robust, and can be applied to any urban settlement in which no proper record of the sewer network is available.

Effectiveness of bioretention system and vegetated swale for reducing urban flood risk in equatorial region: a case study in Kuching, Malaysia

One of the main causes of urban inundation is the rise of impermeable surfaces brought on by increasing urbanization. Low impact development (LID) practices have been employed in previous studies to mitigate urban flooding. However, the effectiveness of LID practices in reducing runoff peaks and improving water quality is unknown, especially in the equatorial region. This study explored nine alternative scenarios to evaluate the effectiveness of the bioretention system and vegetated swale using the Storm Water Management Model (SWMM). Using precipitation data of December 2021, the Swinburne University of Technology Sarawak Campus has been chosen as the case study. The findings demonstrated that these two LID practices could significantly lessen urban flooding. Under scenario 7, the combination of 28.4% bioretention system and 11.3% vegetated swale reduced the maximum runoff peaks by 22.98% at Peak A, 24.71% at Peak B, and 24.09% at Peak C. In the meantime, under scenario 7, the implemented LID practice has removed 20.09% of TSS, 19.75% of TP, and 12.26% of TN. It was discovered that runoff peak reduction increases as the area covered by vegetated swale and bioretention system increases. The outcomes showed that bioretention system performed better than the vegetated swale in reducing peak runoff and enhancing water quality. Local authorities can use the findings of this study to offer recommendations for reducing disaster risk, controlling urban flooding, and revitalizing urban areas.

Urban flood vulnerability Knowledge-Graph based on remote sensing and textual bimodal data fusion

Flood disasters inflict extensive, serious damage on cities and society, significantly constraining urban construction and development. There is an urgent demand to reduce urban flood vulnerability, to explore the evolution mechanism of urban flood vulnerability, and to guide the construction and improvement of urban flood control resilience. An objective and accurate knowledge graph of urban flood vulnerability intuitively, quantitatively, and conveniently expresses the logical relationship between vulnerability indexes. This provides a theoretical and data-based foundation for enhancing urban flood resistance and improving the safety of urban flood control and drainage systems. To address this issue, first, the fusion extraction of text and remote sensing dual-mode data is achieved through technical means such as neural networks. Second, by using multiclass natural language processing (NLP) models, we create an objective index system for urban flood vulnerability that avoids subjective human influence. Finally, we construct an objective weight model group, calculate weights, and then, we establish a vulnerability knowledge graph. The results indicate that (1) by utilizing multidimensional remote sensing images and by adopting the robotic satellite (RoboSat) semantic segmentation model, we achieve high-precision extraction of the remote sensing parameters, such as those for urban roads, terrain, and buildings. Thus, we successfully transform remote sensing data into text data (accuracy is approximately 1 m). (2) We have confirmed the effectiveness of the subjective–objective combined weight method. (3) We introduce a novel approach to create an urban flood vulnerability index system based on bimodal objective data fusion. (4) Utilizing the flood vulnerability knowledge graph, we assess vulnerability levels within the primary urban areas of Zhengzhou City, and we propose governance strategies tailored to the current vulnerability status of each district.

Study of the hydrologic and hydrodynamic coupling model (HHDCM) and application in urban extreme flood systems

Based on urban flood hydrology processes and hydrodynamic principles, the stormwater management model (SWMM) was improved upon. The coupling and implementation methods of the SWMM and two-dimensional hydrodynamic model are proposed. The improved SWMM was coupled with the hydrodynamic model both from the vertical and horizontal directions. The hydrology and hydrodynamic coupling model (HHDCM) was constructed and verified by using extreme rainstorm data. Taking July.20 extreme rainstorms (from July 17 to July 20, 2021, i.e., July.20 extreme rainstorm) in Zhengzhou city, Henan Province, China, as an example and using the HHDCM model, the flood disaster caused by July.20 extreme rainstorm was simulated. Based on the simulation results, an inundation distribution map was drawn for the urban area. A comparison between the simulated and measured results reveals that the maximum relative error in the simulated results is 12.5%. Therefore, the HHDCM model proposed in this paper has desirable accuracy and reliability for simulating extreme urban rainstorms and flood disasters.

Urban flooding: Coping with Weija Dam spillage by downstream communities in Ghana.

In light of these challenges, the authors advocate a close collaborative working partnerships among stakeholders to provide flood risk management interventions, strategic planned dam spillage that minimises the effect of dam spillage-induced floods on the local communities, early warning systems and planning and enforcement of building regulations.

Urbanization Further Intensifies Short‐Duration Rainfall Extremes in a Warmer Climate

AbstractIntensification of short‐duration rainfall extremes contributes to increased urban flood risk. Yet, it remains unclear how upper‐tail rainfall statistics could change with regional warming. Here, we characterize the non‐stationarity of rainfall extremes over durations of 1–24 hr for the rapidly developing coastal megalopolis of the Greater Bay Area, China. Using high‐resolution, multi‐source, merged and gridded data we observe greater increases in rainfall intensities over the north‐central part of the region compared with the southern coastal region. Our results show, for the first time, that urbanization nonlinearly increases rainfall intensities at different durations and return periods. Over short durations (≤3‐hr) and short return periods (2‐yr), urban areas have the greatest scaling rates (≥19.9%/°C). However, over longer durations (≥9‐hr) rural areas have greater scaling rates, with a lower degree of dependency on both durations and return periods.

Progress in Sponge City Research in Yunnan Province

As urbanization accelerates, the challenges faced by traditional urban drainage systems are becoming increasingly prominent. The concept of the "sponge city" has emerged as a new urban development paradigm, playing an increasingly important role in sustainable urban development, alleviating water disasters, and improving the urban water environment. This paper takes Yunnan Province as the research subject and delves into the progress of sponge city research in the region, as well as conducting a comprehensive assessment of the environmental conditions for implementing sponge city construction in Yunnan. The unique geographical and climatic characteristics of Yunnan Province make it an ideal area for implementing the sponge city concept. The paper not only examines in detail the pilot sponge city projects that have been initiated in the province, analyzing the implementation effects of these projects, but also proposes strategies and suggestions for future development based on these case studies. In terms of research methodology, this paper employs a variety of research methods, including literature review, field investigation, and data analysis, to ensure the scientific rigor and comprehensiveness of the research findings. The results show that the sponge city construction in Yunnan Province has achieved certain effects, such as significant progress in urban flood control, rational utilization of water resources, and improvement of the ecological environment. However, the study also identified some problems and challenges, such as gaps between the implementation of some projects and the expected outcomes, as well as difficulties in technology, funding, and policy encountered during the promotion process. Based on these findings, the paper provides targeted suggestions from multiple perspectives, including technological innovation, funding, and policy support, aiming to further promote the development of sponge city construction in Yunnan Province and nationwide. The study emphasizes that Yunnan Province should leverage its unique advantages and formulate appropriate sponge city construction standards and technical guidelines that align with local conditions. It also recommends enhancing interdisciplinary and interdepartmental cooperation to foster a deeper integration of scientific research with urban planning practices, maximizing the benefits of sponge city construction. Overall, this paper's research not only provides valuable experience and insights for the construction of sponge cities in Yunnan Province and other regions of China but also makes an important contribution to the theoretical research and practical exploration of sponge cities. Through an in-depth analysis and systematic study of sponge city construction in Yunnan Province, the paper showcases the tremendous potential and challenges faced by the sponge city concept in specific Chinese practices, holding significant theoretical and practical significance for guiding future urban development planning and management.

Modeling Land Use Transformations and Flood Hazard on Ibaraki’s Coastal in 2030: A Scenario-Based Approach Amid Population Fluctuations

Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its urban development. The study examines the Ibaraki Coastal region to analyze the impacts of land-use changes in 2030, predicting and evaluating future floods from intensified high tides and waves in scenario-based forecasts. The future roughness map is derived from projected land-use changes, and we utilize this information in DioVISTA 3.5.0 software to simulate flood scenarios. Finally, we analyzed the overlap between simulated floods and each land-use category. The results indicate since 2020, built-up areas have increased by 52.37 sq. km (39%). In scenarios of constant or shrinking urban areas, grassland increased by 28.54 sq. km (42%), and urban land cover decreased by 7.47 sq. km (5.6%) over ten years. Our research examines two separate peaks in water levels associated with urban flooding. Using 2030 land use maps and a peak height of 4 m, which is the lower limit of the maximum run-up height due to storm surge expected in the study area, 4.71 sq. km of residential areas flooded in the urban growth scenario, compared to 4.01 sq. km in the stagnant scenario and 3.96 sq. km in the shrinkage scenario. With the upper limit of 7.2 m, which is the extreme case in most of the study area, these areas increased to 49.91 sq. km, 42.52 sq. km, and 42.31 sq. km, respectively. The simulation highlights future flood-prone urban areas for each scenario, guiding targeted flood prevention efforts.

The impact of electrical hazards from overhead power lines on urban search and rescue operations during extreme flood events

Accurate flood forecasting and efficient emergency response operations are vital, especially in the case of urban flash floods. The dense distribution of power lines in urban areas significantly impacts search and rescue operations during extreme flood events. However, no existing emergency response frameworks have incorporated the impacts of overhead power lines on lifeboat rescue operations. This study aims to determine the necessity and feasibility of incorporating overhead power line information into an emergency response framework using Manville, New Jersey during Hurricane Ida as a test bed. We propose an integrated framework, which includes a building-scale flood model, urban point cloud data, a human vulnerability model, and network analysis, to simulate rescue operation feasibility during Hurricane Ida. Results reveal that during the most severe point of the flood event, 46% of impacted buildings became nonrescuable due to complete isolation from the road network, and a significant 67.7% of the municipality’s areas that became dangerous for pedestrians also became inaccessible to rescue boats due to overhead power line obstruction. Additionally, we identify a continuous 10-hour period during which an average of 43.4% of the 991 impacted buildings faced complete isolation. For these structures, early evacuation emerges as the sole means to prevent isolation. This research highlights the pressing need to consider overhead power lines in emergency response planning to ensure more effective and targeted flood resilience measures for urban areas facing increasingly frequent extreme precipitation events.

The zoning of flood disasters by combining tidal flood and urban flood in Semarang City, Indonesia

Semarang City, situated along the northern coastal region of Java Island, Indonesia, grapples annually with the dual challenges of tidal flooding and urban inundation. Establishing a comprehensive flood disaster zoning for both tidal floods and urban inundation to identify priority areas for effective flood mitigation and reducing disaster risk. This study aims to assess the potential inundation of tidal floods, urban floods, and their combined impact on Semarang City, using an integrated approach that combines Geographic Information System (GIS) technology with the Analytic Hierarchy Process (AHP). By leveraging data from Digital Elevation Model Nasional (DEMNAS), Google Earth, Rupa Bumi Indonesia (RBI) maps, and the Open Street Map (OSM) database, we obtained elevation, landcover, distance to the river, buildings, and administration data, which were used as criteria in the analysis. The research findings unveil three distinct flood inundation scenarios within Semarang City: tidal floods, urban floods, and a combination of both. Each scenario is categorized into low, medium, and high levels of inundation, providing a granular assessment of flood risk across the city. Importantly, the study extends beyond spatial analysis to evaluate the impact of inundation on affected buildings. The results indicate that the area inundated by the combination of tidal and urban floods is as follows: 86.87% high inundation, 13.12% moderate inundation, and 0.01% low inundation.

A preliminary assessment on spatial prioritization for integrated urban flood management based on source tracking method

With rapid urbanization and global climate change, the frequency of extreme rainfall and urban flooding has increased significantly, posing a serious threat to human life and property safety. Integrated urban flood management is an important component of urban planning that combines various infrastructure to effectively manage and utilize stormwater. This is essential for dealing with urban inundation disasters. Despite the implementation of numerous engineering and non-engineering measures, flood mitigation still faces a challenge in identifying spatial prioritization due to the lack of consideration for regional spatial connectivity. Taking Jinan City, China, as a case study, this research combined the flood source tracking method with the hydrodynamic model LISFLOOD-FP to identify the inundation area and its sources. Additionally, this study analyzed spatial hydrological connectivity was also analyzed in. The spatial prioritization was classified based on four intervention criteria: (Criterion 1) flooding extent; (Criterion 2) maximum inundation depth; (Criterion 3) flooding road network; (Criterion 4) flood exposure to buildings. Furthermore, the prioritization results of various combinations of criteria were also analyzed. Finally, the impact of rainfall intensity and grid cell size on the spatial prioritization was discussed. The results of this study emphasize the importance of spatial hydrological connectivity and flood sources. Various intervention criteria determine different spatial prioritization, highlighting the importance of developing multi-intervention criteria based on specific needs and objectives. The results indicated that sponge city pilot area in Jinan City has a high spatial prioritization. Rainfall intensity and grid cell size strongly influence the spatial prioritization, while these factors have little impact on their spatial distribution. This study demonstrates the novelty of combining intervention criteria and flood source tracking method and provides new perspectives and valuable references for urban flood mitigation.

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.