Flood Hazard Research Articles

Downstream impacts of dam breach using HEC-RAS: a case of Budhigandaki concrete arch dam in central Nepal

Studies on concrete dam breach are limited compared to earthen and other types of dams. With an increase in the construction of concrete dams, particularly in the developing world, it is imperative to have a better understanding of the dam breach phenomena and the identification of the most influential breach parameters. This study aims to contribute to this gap by taking the case of the concrete arch dam proposed for the 1200 MW Budhigandaki Hydropower Project located in central Nepal. This study carries special significance for Nepal, primarily because of the increasing number of under construction and proposed large dams for water resources development in the country. We carry out dam breach analysis of the Budhigandaki dam using HEC-RAS 2D model to calculate the flood discharge peaks, time to peak, water surface elevation and the extent of inundation for two scenarios (with and without probable maximum flood) to estimate the damage on four downstream settlements. We carry out sensitivity analysis of the breach parameters on the flood magnitudes and severity. Results show that all the study locations lie in the high flood hazard zone. Flood peaks can reach as high as 286,000 m3s− 1 to 511,000 m3s− 1 in the considered settlements. The time to peak ranges from 11.3 to 17 h after the breach at these locations. We estimate that if a breach should happen, it would most likely inundate around 150,000 buildings, impact nearly 672,000 lives and flood 3,500 km of road downstream. Furthermore, dam breach elevation is found to be the most sensitive parameter to downstream floods. Hence, rather than structural measures, it is recommended that non-structural measures are implemented for minimizing the impacts of flood disasters at the study locations. The findings could be a useful reference for future dam projects in Nepal and other areas with similar hydrological and topographical conditions.

Geospatial approach to pluvial flood-risk and vulnerability assessment in Sunyani Municipality

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

Modern Methods and Solutions for Water Resources Management in the Wadi-Watir Region (Egypt)

The main problem of water resources in Egypt is the increasing demand and decreasing supply, especially after the construction of the Ethiopian Renaissance Great Dam. One of the best solutions is the use of unusual alternative management techniques, including as a major one, rainwater harvesting and distribution. This paper examines the use of unconventional approaches to water management in arid regions based on rainfall accumulation. Using Geographic Information Systems (GIS) and hydrological modelling, the suitability of the Wadi-Watir catchment (Egypt) for rainwater storage was assessed and optimal locations for the construction of storage structures were identified. The results of the study showed that 19% (666 km2) of the study area is highly suitable for the aims and objectives; 16% (573 km2) of the catchment area has limited realisability. Twelve sites were found to be suitable for the construction of storage dams. Fourteen sites are identified as optimal for the placement of percolation (filtration) reservoirs along watercourses. The area considered ideal for above-ground percolation (filtration) tanks is 25.9 km2. The optimum area for farm ponds is 1.34 km2. As a result, the construction of water storage structures is proposed to improve water management in the Wadi-Watir region. The implementation of the rainwater harvesting structures (SRF) was divided into three phases depending on the severity of the flash flood hazard. The first, second and third phases can meet 62.24% (34.24 mil m3) of the water demand. This approach is a new and modern solution to the problem of water scarcity under socio-economic and environmental pressures while achieving the goals of sustainable development in Egypt. The article is based on the dissertation research of Mohamed Mostafa Ezzeldin Abdelrahim (MGSU, dissertation council № 24.2.339.07, March 2024).

The role of Nature-Based Solutions for the water flow management in a Mediterranean urban area

Due to climate change and rapid urban sprawl, central Mediterranean regions are subjected to short but intensive storm events resulting in a significant increase of flow rates and runoff volumes in low-lying coastal urban areas. Within the framework of green urban infrastructures (GUIs), a suitable combination of Nature-Based Solutions (NBS) with traditional grey infrastructures should be adopted to mitigate flood risk effects in urban and sub-urban areas contributing at the same time to achieve multiple benefits for both the environment and the community. The aim of this study is (i) to evaluate flood hazard maps for identifying the areas within a Sicilian hydrological river basin (Toscano catchment) with a high risk of flooding by implementing the HEC-RAS model at catchment scale; (ii) to quantify the peak flow and floodplain area reduction; (iii) to assess the effectiveness of small-scale NBS (green roofs, porous pavements, rain gardens and rain barrel), in terms of peak flow and runoff volume reductions, at urban block scale by using the EPA SWMM model Model simulations are performed integrating the hydraulic (HEC-RAS) and hydrological (EPA SWMM) models for return periods of 10, 50 and 200 years. The results showed that the estimated peak flow (m3 s−1) obtained from the simulations performed at catchment scale for each T in the current scenario (without NBS) using HEC-RAS and EPA SWMM models are very close to each other (R2 = 0.99). In addition, the utopian scenario simulation showed the HEC-RAS model sensitivity to CN calibration achieving a peak flow reduction up to about 60% with the floodplain area decreasing up to about 17%. Finally, the model EPA SWMM shows its sensitivity to NBS implementation at urban block scale with a peak flow reduction up to about 16% and a runoff volume (mm) reduction up to about 24%. These reductions decrease as T increases with a higher NBS mitigation effects for the lowest T. The results highlighted that the integration of NBS in urban areas could have hydrological and hydraulic positive effects, particularly in terms of peak flow and runoff volume reduction. Furthermore, the results suggest that small-scale NBS have a potential to be effective to smaller rainfall events, but a combination with large-scale NBS is necessary to cope with extreme events.

Discerning the dynamics of urbanization-climate change-flood risk nexus in densely populated urban mega cities: An appraisal of efficient flood management through spatiotemporal and geostatistical rainfall analysis and hydrodynamic modeling

While the contribution of climate change towards intensifying urban flood risks is well acknowledged, the role of urbanization is less known. The present study, for the first time in flood management literature, explores whether and how unplanned-cum-urbanization may overshadow the contribution of extreme rainfall to flood impacts in densely populated urban regions. To establish this hypothesis and exemplify our proposed framework, the National Capital Territory (NCT) of Delhi in India, infamous for its concurrent flood episodes is selected. The study categorically explores whether the catastrophic 2023 urban flood could have resulted in a similar degree of urban exposure and damage, had it occurred anytime in the past. A comprehensive spatiotemporal and geo-statistical analysis of rainfall over 11 stations brought about through Innovative trend analysis, Omnidirectional and directional Semi-variogram analysis, and Gini Index indicates a rise in extreme rainfalls. High-resolution land-use maps indicate about 39.53 %, 52.66 %, 56.60 %, and 69.18 % of urban footprints during 1993, 2003, 2013, and 2023, while gradient direction maps indicate a prominent urban surge towards the North-West, West, and Southwest corridors. A closer inspection of the Greenness and Urbanity indices reveals a gradual decline in the green footprints and concurrent escalation in the urban footprints over the decades. A 3-way coupled MIKE+ model was set up to replicate the July 2023 flood event; indicating about 13 % of the area experience “high” and “very-high” flood hazards. By overlaying the flood inundation and hazard maps over land-use maps for 1993, 2003, and 2013, we further establish that a similar flood event would have resulted in lesser damage and building exposure. The study offers a set of flood management options for refurbishing resilience and limiting flood risks. The study delivers critical insights into the existing urban flood management strategies while delving into the urban growth-climate change-flood risk nexus.

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques

Urban flood risks have intensified due to climate change and dense infrastructural development, necessitating innovative assessment approaches. This study aimed to integrate advanced hydrodynamic models with machine learning (ML) techniques to improve urban flood prediction and hazard analysis. Integrating 1D and 2D hydrodynamic models calibrated with precise parameters demonstrated exceptional predictive accuracy for flood dynamics. The high-resolution Lidar images and sophisticated modeling were used to simulate flood scenarios for the River Thames in West London. The socioeconomic data were incorporated to map vulnerable zones accurately. The findings revealed that 2D hydrodynamic models, while computationally intensive, offered superior accuracy in predicting flood dynamics. In contrast, 1D models could have been more accurate in predicting inundation depth and extent in adjacent urban areas. Advanced ML models, such as the Extra Trees-Principal Component Analysis (ET-PCA) model, further instilled confidence in the reliability of the research findings, demonstrating near-perfect predictive reliability with an R2 of 0.999. This model provided near-perfect predictions and enabled precise flood risk zoning. The integration of socioeconomic data further highlighted the vulnerability of certain urban areas. It emphasized the importance of targeted flood mitigation strategies. Beyond the specific case study, this research demonstrated the potential of combining hydrodynamic simulations with ML to enhance urban flood resilience globally. This framework helps urban planners and policymakers devise effective flood mitigation strategies and improve infrastructure resilience against future flood events.

Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia

ABSTRACT Increased frequency and magnitude of flooding pose a significant natural hazard to urban areas worldwide. Mapping flood hazard areas are crucial for mitigating potential damage to human life and property. However, conventional hydrodynamic approaches are hindered by their extensive data requirements and computational expenses. As an alternative solution, this paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables. We employed various ML classifiers, including decision tree (DT), random forest (RF), XGBoost (XGB), and k-nearest neighbor (kNN), to assess their performance in flood hazard mapping. Model evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). Our results demonstrated promising outcomes, with AUC values of 93% (DT), 97% (RF), 98% (XGB), and 91% (kNN) for the validation dataset. RF and XGB have slightly higher performance than DT and kNN and distance to river was the most important factor. The study highlights the potential of ML for urban flood modeling, offering reasonable accuracy and supporting early warning systems. By leveraging available geo-environmental variables, ML techniques provide valuable insights into flood hazard mapping, aiding in effective urban planning and disaster management strategies.

How trust in the government's flood response influences perception of flood hazard risk: Experimental evidences in China

How trust in the government's flood response influences perception of flood hazard risk: Experimental evidences in China

Evaluating socio-hydrological stress through potential stormwater harvesting sites using multi-criteria analysis in Mumbai, India

Estimating stormwater harvesting (SWH) is a significant characteristic of enhancing regional water obtainability and thereby preserving water resources. The foremost aim of this study was to approximate suitable SWH locations in Mumbai using several physical and geomorphic parameters by employing the Geographical Information System (GIS) based Analytical Hierarchy Process (AHP) method. Several observed and remotely sensed datasets were used to compute SWH after assigning the share of influence at GIS platform. Numerous physical attributes along with geomorphic features are taken into consideration and further reclassified and allocated ranks to produce the SWH map in Mumbai. The study indicates most suitable portion for SWH is associated with northwestern side of Mumbai along with barren land and vegetation cover of around 226 sq. km area. However, highly dense urban areas (182.5 sq. km) in central Mumbai experienced with low suitable SWH zone. Additionally, LULC-wise investigation exhibited that barren and wetlands are the most suitable part for SWH, averaging 62.7 sq.km, however, nearly coastal areas including central Mumbai, and some open urban spaces are also potentially suitable for SWH. Moreover, flood probability-wise SWH site demonstrates maximum suitability near the upper parts of coastal Mumbai (223.3 sq.km), while 82.9 sq.km area witnessed flood possibility. High-resolution Google Earth images were used to overlap the outcome of SWH in Mumbai. Moreover, the SWH sites were further cross-correlated with regional flood susceptibility, which exposed extreme north and northeastern parts of Mumbai and is eventually essential to yield SWH sites to diminish flood hazards and substantial water availability.

Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai

Abstract. Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of a driver's individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the same frequency amplification (SFA) method to transform the design event values in hourly time series so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver, and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, that the peak rainfall occurs 2 h before the peak storm surge would cause the deepest average cumulative inundation depth. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagates upstream in the Huangpu River, resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.

Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates

Accurate and timely information on building age is essential for mitigating the impacts of natural disasters such as earthquakes and floods. Traditional methods for collecting these data are often inefficient and costly. This study leverages remote sensing and machine learning to classify building age and integrate this information into a comprehensive flood hazard map for Al Ain City. By combining building age with elevation, topography, land use, population density, and other factors, we generated a flood hazard map that effectively identifies high-risk areas. Our results show that a significant portion of the city is has low potential for flooding (61%), while others face moderate (32%), high, or very high flood hazards. This study provides crucial information for informed decision-making regarding future urban planning, disaster management, and development strategies in Al Ain City. By identifying vulnerable buildings in advance, it supports efforts to reduce disaster risks, protect lives, and optimize resource allocation.

Probabilistic Cascade Modeling for Enhanced Flood and Landslide Hazard Assessment: Integrating Multi-Model Approaches in the La Liboriana River Basin

Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential.

Simulating blue and green infrastructure tools via SWMM in order to prevent flood hazard in an urban area

Simulating blue and green infrastructure tools via SWMM in order to prevent flood hazard in an urban area

Assessment of the spatial and temporal dynamics of food system resilience and its response to natural hazards

Increasingly frequent natural hazards put food systems under constant stress, and there is an urgent need to improve the resilience of food systems. Quantitative assessments of food system resilience (FSR) and research on the factors that influence it are important ways to achieve this goal. In this research, we use probabilistic modeling to quantitatively assess the FSR of forty counties in the southeastern Tibetan Plateau from 2000 to 2020, and explore the spatiotemporal dynamics characteristic of FSR. The Geographically and Temporally Weighted Regression (GTWR) model is then used to examine the drivers of FSR from four dimensions: shocks, natural ecology, production conditions, and socioeconomics. The focus is on the spatial and temporal characteristics of the effects of drought, flood, wind hail, snow disaster, earthquake, and geological hazards on FSR. The results of this study highlight the spatiotemporal heterogeneity of FSR and its differential response to different hazards: 1) the assessment of FSR accurately pinpoints areas of low resilience at both spatial and temporal scales, and reveals a significant temporal phase characteristic, with most regions experiencing a notable increase in FSR since 2010; 2) High-intensity droughts and snow disasters, and frequent floods and wind hails significantly affect FSR, so it is necessary to plan targeted measures to mitigate the negative impacts of different natural hazards on food production systems. Our findings enhance the understanding of the relationship between food systems and natural hazards, and contribute to the planning of measures to safeguard food system stability and improve food system resilience to natural hazards in natural hazard-prone regions.

A robust ensemble of hybrid and bivariate statistical models for flood prediction mapping in Lower Damodar River Basin of India

This research explores flood prediction in the Lower Damodar River Basin (LDRB) using a hybrid ensemble of a Naïve Bayes Tree (NBT) and five bivariate statistical models such as Evidential Belief Function (EBF), Index of Entropy (IOE), Frequency Ratio (FR), Statistical Index (SI), and Modified Information Value (MIV). A total of 348 flood locations and 15 conditioning factors including hydrological, topographical and land cover were considered for this analysis. To ensure the precision of model predictions, a multicollinearity assessment was executed. Receiver operating characteristic (ROC) curve, area under curve (AUC), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were performed to compare and asses each of the models all. The results reveal that all models performed well in creating flood hazard maps with AUC >0.8 and RMSE <0.4. FR and EBF models demonstrated the highest predictive accuracy (AUC=0.85), followed by the IOE, SI, and MIV models. The ensemble of NBT with bivariate models shows promising results, showcasing reduced error metrics and improved accuracy for the IOE, SI, and MIV models. This study highlights the potential of ensemble models in flood hazard prediction, offering valuable insights for global flood risk management. The successful application of these data-driven models showcases their importance in forecasting flood risks, aiding decision-makers and planners in developing more effective flood mitigation strategies.

Behavior Problem and Mental Health of Children Affected by Floods-2022 in Pakistan

Objectives The present study aimed to examine the association between socio-demographic characteristics, damage to perceived needs, post-traumatic stress symptoms, general effect of flood and behavior problems among flood-affected children. Method Cross-sectional research design was used. A sample of 76 children (aged 8–16 years, experienced extreme hazard flood event) was collected from two schools in a flood-affected rural community in South Punjab-Pakistan through purposive sampling. Children revised impact of event scale-13 and a pediatric symptom checklist were used to screen for post-traumatic stress disorder (PTSD) and internalizing, externalizing or inattention behavior problems respectively. Results Primary school children who reported financial loss in terms of damage to houses, difficulty faced by parents in building new houses and living in tents were more affected by the flood. Age was a significant positive predictor of the general effect of flood on children. Female children positively predicted arousal PTSD and pediatric behavior problems including inattention problems. Moreover, children who lost their pets had more post-traumatic stress symptoms, as well as externalizing and inattention behavior problems. Number of times previously exposed to floods positively predicted arousal PTSD. Post-traumatic stress symptoms positively predicted behavior problems. Conclusion Primary school children affected by a flood are at greatest risk of developing behavior and mental health problems.

Investigating the Effect of Changes of the Roughness Coefficient on the Results of Model Calculations in One-dimensional Hydraulic Models

One of the key elements in describing the flow of floodwaters is to determine the resistance created by friction acting along the surface wetted by water and created by objects directly washed by water. Flow resistance (roughness) influences flow velocity, water level, and, consequently, the river's capacity (Aberle 2020). This paper analyses the effect of the manner of defining roughness coefficients in one-dimensional models on the stability of these models and the results of model calculations. Three methods of defining the transverse variation of roughness coefficient in cross sections were used in the model calculations: Distributed, High/Low Flow Zones and Uniform. The greatest model stability, the highest flow rate values and the lowest water table ordinates were obtained when Distributed was selected. This has a major impact on determining the extent of flood hazard zones.

A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness

The impact of climate change and urbanization has increased the risk of flooding. During the UN Climate Change Conference 28 (COP 28), an agreement was reached to establish “The Loss and Damage Fund” to assist low-income countries impacted by climate change. However, allocating the resources required for post-flood reconstruction and reimbursement is challenging due to the limited availability of data and the absence of a comprehensive tool. Here, we propose a novel resource allocation framework based on remote sensing and geospatial data near the flood peak, such as buildings and population. The quantification of resource distribution utilizes an exposure index for each municipality, which interacts with various drivers, including flood hazard drivers, buildings exposure, and population exposure. The proposed framework asses the flood extension using pre- and post-flood Sentinel-1 Synthetic Aperture Radar (SAR) data. To demonstrate the effectiveness of this framework, an analysis was conducted on the flood that occurred in the Thessaly region of Greece in September 2023. The study revealed that the municipality of Palamas has the highest need for resource allocation, with an exposure index rating of 5/8. Any government can use this framework for rapid decision-making and to expedite post-flood recovery.

How urban form impacts flooding

Urbanization and climate change are contributing to severe flooding globally, damaging infrastructure, disrupting economies, and undermining human well-being. Approaches to make cities more resilient to floods are emerging, notably with the design of flood-resilient structures, but relatively little is known about the role of urban form and its complexity in the concentration of flooding. We leverage statistical mechanics to reduce the complexity of urban flooding and develop a mean-flow theory that relates flood hazards to urban form characterized by the ground slope, urban porosity, and the Mermin order parameter which measures symmetry in building arrangements. The mean-flow theory presents a dimensionless flood depth that scales linearly with the urban porosity and the order parameter, with different scaling for disordered square- and hexagon-like forms. A universal scaling is obtained by introducing an effective mean chord length representative of the unobstructed downslope travel distance for flood water, yielding an analytical model for neighborhood-scale flood hazards globally. The proposed mean-flow theory is applied to probe city-to-city variations in flood hazards, and shows promising results linking recorded flood losses to urban form and observed rainfall extremes.

A hybrid surrogate model for real-time coastal urban flood prediction: An application to Macao

Tropical cyclones could cause co-occurrences of storm tides and rainfall and, therefore, induce flooding hazards, posing high risks to coastal cities. Accurate and timely prediction of flood hazards in time–space domain is essential for flood risk management. In this study, a hybrid surrogate model is proposed for real-time flooding prediction by considering the compound effects of storm tides, rainfall, and drainage outflows. The hybrid surrogate model combines a long short-term memory (LSTM) neural network for predicting drainage outflows with a one-dimensional convolutional neural network (1D CNN) for predicting water depths. The model is tested using performance metrics, and the simulation results agree well with the historical measurements. Moreover, a sensitivity analysis of the drainage outflows using the modified Morris screening method reveals the importance of considering drainage systems in flood modelling, particularly when rainfall return periods are relatively small. The sensitivity indices vary over the computation domain in terms of different driving factors, with positive values corresponding to rainfall-dominated area while negative values corresponding to storm tide-dominated area. The computation cost of the model is quite low, and the fast prediction speed could provide a sound basis for early warning and real-time flood management. The proposed hybrid surrogate model offers a promising function for rapid and accurate flood inundation prediction in coastal cities, enabling more effective flood risk management and emergency response planning.