Storm Water Management Model Model Research Articles

Optimizing urban green infrastructure using a highly detailed surface modeling approach

Urban trees and forests show a better ecosystem with many benefits, including pure air quality. The development of urban green infrastructure (UGI) creates a better management system that greatly impacts social values in an urban system. The UGI and construction activities are receiving much attention for their effectiveness in addressing various urban ecological, social and economic issues. Using green infrastructure in stormwater management can reduce the influence on urban sewerage systems and, eventually, on building water resources. The main goal of the research is to optimize the green infrastructure to provide a less-pollution, well-organized, and pleasurable environment for the inhabitants. Various models are used to study the present rainfall-runoff scenario, but the stormwater management model (SWMM) is the most preferable and suggested model. Once the parameters are accessed, optimizing the green infrastructure (GI) will be easy. A complete SWMM model is evaluated over the complete surface, and a hydrological measurement is presented. The evaluation study presents various component percentages: asphalt (37%), green (27%), ceiling (21%), grit (12%), and cemented area (2%), which provides rainproof coverage of approximately 60%. A design is developed about the diverse events of GI in urban exploiting the SWMM and demonstrates its stimulus on the rainfall-runoff behaviour. In recent years, very little attention has been given to green spaces in urban areas, which not only increases pollution but also decreases the urbanization. Therefore, urban green spaces are more important to improve air quality and resident living standards. Over the given scenario and the rainfall event, a decline of the quantitative discharge parameters is evident, such as discharge volume (i.e., from 3.6 to 61.8) and the peak discharge rate (i.e., from 5.4 to 62.7%). The simulation results show that green areas give high satisfaction with low construction costs, which shows the superlative performance ratio of the examined measures. From the investigation, it is also recommended to have green areas and public spaces in impervious urban areas, which greatly reduced the runoff in the project area.

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.

Developing data-driven learning models to predict urban stormwater runoff volume

ABSTRACT The Storm Water Management Model (SWMM) is a hydrological model for simulating and predicting runoff. Although powerful, SWMM can be computationally demanding. Therefore, we develop machine learning (ML) models to approximate the behavior of SWMM and expedite the task of predicting runoff. We perform a case study for the First Creek watershed in Knoxville, Tennessee, USA. We train ML models using rainfall data and subcatchment characteristics and apply feature engineering and clustering to objectively compare the outputs from SWMM and ML models. The results show that random forests can predict runoff volume accurately, with a Mean Absolute Error (MAE) of 0.006 (0.001) 10 6 gallons, where predictions are made almost instantaneously. Hence, our proposed ML-based approach can accurately predict runoff while greatly reducing computational requirements, filling a critical need in the field.

Systematic Assessment on Waterlogging Control Facilities in Hefei City of Anhui Province in East China

Both the renovation of rainwater pipes and the addition of sponge city facilities in the low-terrain residences of urban fringes were rarely systematically simulated using the Storm Water Management Model (SWMM). With the waterlogging prevention project in an old residential quarter at a fringe of Hefei city being an example, this study used the SWMM to simulate the effect of the renovation of rainwater pipes and sponge city facilities under different return periods. The results showed the key nodes on the main pipes met the drainage requirements based on water depth analysis after renovation below the 20-year return period, and the reduction rate of the maximum water depth at the key node J5 was the greatest, with 87.7%. The four flow parameters (the average flow rate, the peak flow rate, the total discharge, and the percentage of water flow frequency) for the two outlets (PFK1 and PFK2) all improved after renovation under five return periods (2, 5, 10, 20, and 50 years [a]). The addition of sponge city facilities effectively reduced the amount of rainwater runoff from 28.68% to 14.78% during 2 a to 50 a, and the maximum reduction rate of water depth, being 61.15%, appeared in J5 under 20 a. The curve integral area of the depth over the elapsed time was innovatively used to indirectly express the accumulated rainwater volume through the rainwater well. This study verified that the SWMM model can be well applied to old low-terrain residential quarters in urban fringes and broadened the application scenario of the model.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Inconsistent using FLOOD and Flooding Development of EPA SWMM for Assessing Flood Occurrences in Vulnerable Urban Watershed Considering Extreme Rainfall Events

Urban flood, commonly known as urban water congestion, is a type of water hazard that poses significant challenges for urban residents and water management experts. Chittagong, an essential economic hub in Bangladesh, renowned for its role as a port city, comprises a diverse range of land use, including residential, industrial, and commercial sectors. The Chaktai canal, an important element of Chittagong drainage system, is connected to the Karnaphuli river, playing a vital role in managing drainage by handling a substantial portion of the city water. Therefore, this research evaluated the operational efficiency of a specific drainage network under the influence of altered rainfall events using the Storm Water Management Model (SWMM). Using ArcGIS 10.4, the land use pattern of the area was researched, incorporating data from field surveys and secondary sources. SWMM 5.1 integrated watershed data, and further simulation was carried out to estimate runoff in various sub-catchments and drainage network limitations during heavy rain. During the intense monsoon period, the tool determined the average runoff depth, considering backwater effects and robust tidal surges, resulting in a depth of 3.3m compared to 2.6m in the dry season. This research evaluated the influence of impervious land use changes on urban drainage systems. While meteorological factors alone render drainage network sufficient in dry periods, the outfall shows vulnerability during the rainy season, with an allowance of only 0.7m, jeopardizing the catchment through flood. It contributed a schematic sub-catchment representation, emphasizing that flood events depend on volume runoff and peak flow in urban drainage system. SWMM model was used to illustrate the catchment surface runoff and interconnected node depths via conduits, as well as the current catchment scenario comprehensively.

Development of a Microservice-Based Storm Sewer Simulation System with IoT Devices for Early Warning in Urban Areas

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.

A 1D-2D dynamic bidirectional coupling model for high-resolution simulation of urban water environments based on GPU acceleration techniques

Urban water pollution is an important issue of global concern. An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. Therefore, we developed a 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the entire urban water environment (GAST-GCSE) comprising the graphics processing unit (GPU)-accelerated surface water flow and transport (GAST) model and the storm water management model (SWMM). The SWMM and GAST model were used for coupled simulation of hydrological and hydrodynamic quantities and water quality in 1D and 2D regions, respectively. The time steps of the two models are the same. Compute Unified Device Architecture (CUDA) parallel architecture programming and advanced model algorithms were adopted to improve the simulation accuracy and efficiency of the GAST model. The two models were dynamically coupled by calling the dynamic link library (DLL) in the SWMM and adding external functions. Two test cases were considered to verify the performance of the proposed model. The results showed that the proposed model provides high simulation accuracy. Compared with the results obtained by commercial software and theoretical values, the relative errors of the water quantity and water quality simulation results were less than 3%. The whole water environment of the main urban area of Changzhi city in North China was evaluated by the GAST-GCSE model, and the simulation results were compared with monitoring data and pure SWMM simulation results. The simulation errors at three waterlogging points were less than 14%, and the Nash efficiency coefficients (NSE) of the NH4+-N concentration in the three river sections were 0.77, 0.56 and 0.64, respectively. Notably, the model needed only 4.39 h to simulate rainfall production and confluence on a 2D surface, a river flood in complex terrain and its accompanying pollutant evolution process involving 3,849,428 cells over 4 h. This study provides new insights, ideas and tools for urban water environment simulation and change mechanism study.

An alternative for predicting real-time water levels of urban drainage systems

Storm Water Management Model (SWMM) developed by the United States Environmental Protection Agency (EPA) has been widely applied throughout the world for analysis associated with stormwater runoff, combined sewers, and other drainage facilities. To appropriately manage the runoff in urban areas, an integrated system including the simulations of sewer flow, street flow, and regional channel flow, called the 1D/1D SWMM model, was advocated to be performed. Nevertheless, the execution efficiency of this integrated system still needs to be promoted to meet the demand for real-time forecasting of urban floods. The objective of this study is to seek an alternative for predicting water levels both in the sewer system and on the streets within an urban district during rainstorms by utilizing a dynamic neuron network model. To strengthen the physical structure of the artificial intelligence (AI) model and simultaneously make up for the lack of measured data, simulation results of the 1D/1D SWMM model are provided as labels for the training of the proposed model. The novelty of this research is to propose a new methodology to effectively train the AI model for predicting the spatial distributions of depths based on the hydrologic conditions, geomorphologic properties, as well as the network relation of the drainage system. A two-stage training procedure is proposed in this study to consider more possible inundation conditions in both sewer and street (open channel) drainage networks. The research findings show that the proposed methodology is capable of reaching satisfactory accuracy and assisting the numerical-based SWMM model for real-time warning of drainage systems in the urban district.

ASSESSMENT OF THE RISK OF FLOODING IN AN URBANIZED AREA IN RESPONSE TO HEAVY RAINS: CASE STUDY OF THE WARSAW CHOPIN AIRPORT

The paper presents the adaptation of the hydrodynamic Storm Water Management Model(SWMM) for the subcatchment area of the Służewiecki Stream in Warsaw, the major part of whichis the area of the Chopin Airport. The SWMM model was used for calculating the water outflowfrom the studied urbanized area in response to rainfall with the probabilities of 20 and 10%, andthen to assess the possibility of flooding as a result of rainwater spillage from the Airport drainagesystem. The scope of the work also comprised assessing the impact of the existing retention tanksat the Airport on the reduction of maximum flows in the Służewiecki Stream channel. Simulationsin response to the rainfall event with a probability of 10% have shown that there are locations(sewer manholes) within the airport area where short-term rainwater overflows may potentiallyoccur. Retention of rainwater in tanks and flow control through gate valves have a significantimpact on a reduction of flows in the Słuzewiecki Stream, thus reducing the risk of flooding in thestudied catchment area.

Enhancing Urban Surface Runoff Conveying System Dimensions through Optimization Using the Non-Dominated Sorting Differential Evolution (NSDE) Metaheuristic Algorithm

Rapid urban development and increase in construction have significantly altered the surface coverage of cities, resulting in a rise in impervious surfaces such as roofs, streets, and pavements. These changes act as barriers against rainwater infiltration into the soil, leading to a substantial increase in surface runoff. Managing surface runoff has become a critical task in civil engineering and urban planning, as it can mitigate damage and provide opportunities for utilizing excess water. However, traditional flood control and guidance systems tend to be extensive and expensive, prompting researchers to explore cost-effective alternatives that consider all design parameters and variables. In this research, we propose an innovative approach that combines the NSDE (non-dominated sorting differential evolution) metaheuristic algorithm as an optimizer with the SWMM (storm water management model) as a simulator. The objective is to design efficient surface runoff collection networks by thoroughly investigating their hydraulic behaviors. This study focuses on the Chitgar watershed in Tehran, Iran, utilizing the SWMM model and NSDE multi-objective metaheuristic algorithm to determine the optimal dimensions of the channel and its intersecting structures. The aim is to minimize costs and reduce water leakage from the network. A comparison is made between the optimized design results and the existing network plan (without any design modifications). The analysis reveals substantial reductions in water leakage for all three design scenarios: a 7.66% reduction when considering only bridges, a 7.35% reduction with only the canal, and an impressive 95.26% reduction when both the canal and bridges are incorporated. These findings demonstrate the superiority of the optimized designs in terms of cost-effectiveness and the efficient management of surface runoff.

Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Hydrological models have been used to understand the transportation of water quantity and quality in drainage systems, and the stormwater management model (SWMM) is one of the most widely-used models for runoff quantity and quality simulations in urban areas. Although significant efforts have been made to identify the appropriate input parameters of the SWMM model in various watersheds, it is difficult to reflect the variability of the real environment using a fixed input parameter. Data assimilation (DA) is a compatibility method that improves prediction accuracy by merging observations and simulation outputs. It is useful to reduce the forecast error from temporal transferability with information interaction between the model output and observation data. However, conventional DA approaches cannot completely overcome their unrealistic assumptions such as linearity, normality, and error covariances. To address these challenges, we used deep reinforcement learning (RL) to develop an automatic assimilation model that interactively optimizes the SWMM parameters in a real environment (SWMM-RL). In the SWMM-RL model, the agent is trained by rewarding and/or punishing the action (modulated SWMM input parameter) in real time according to the state changes. The model was constructed to minimize the error of runoff and pollutant load simulation of suspended solids (SS), total nitrogen (TN), and total phosphorus (TP) in each stormwater monitoring event. The results demonstrated that the SWMM-RL model primarily outperformed the fixed input parameter SWMM model (SWMM-PS) in simulating the runoff and the three different pollutant loads (the median value of Nash–Sutcliffe efficiency (NSE) for 10 stormwater events was increased by 0.11, 0.31, 0.36, and 0.07 for runoff, SS, TN, and TP, respectively). The SWMM-PS has disadvantages in simulating low rainfall events because of the high sensitivity of runoff peaks to heavy rainfall conditions. Furthermore, the effects of extreme rainfall events were estimated using the SWMM-RL model. This study showed how SWMM-RL combined with the DA method increased forecast accuracy by providing sensitivity and temporal transitions of input parameters.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c1) and exponent (c2) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. This study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

Robustness of storm water management model parameter sets for dry and wet hydroclimatic conditions

The Storm Water Management Model (SWMM) is an urban watershed model for simulating urban runoff quantity and quality. Although SWMM has been widely used, little to no research has explored the effect of fitting the same SWMM dataset to multiple hydrologic conditions. This paper assesses the impact of best-fit parameter estimates on the hydrologic responses of dry and wet hydroclimatic conditions, evaluates parameter uncertainty, and identifies the physical processes that govern parameter changes during dry and wet conditions. We compared the ability of SWMM to predict flows when independently calibrated to a dry and a wet year, respectively, using an automatic calibration program for SWMM, OSTRICH-SWMM (Optimization Software Toolkit for Research Involving Computational Heuristics). The model developed using SWMM calibrated to a wet year performed better during the model assessment period. The best fit estimates of SWMM parameters differed significantly between dry and wet years. For instance, Manning's roughness coefficient for overland flow was higher in a dry year, as less runoff meant less flow depth on surfaces. Some parameters, e.g., % effective imperviousness, exhibited an expanded posterior probability distribution, increasing the uncertainty of the parameter estimate. However, other parameters, such as Manning's roughness coefficient for streams were the least sensitive and well-defined as the width of their posterior distribution only spanned a relatively narrow range which did not change drastically between the two cases. The robust assessment conducted in this study demonstrated that the hydrologic behavior of SWMM was different between models developed and calibrated for dry and wet hydroclimatic conditions. The different optimal parameter values suggest that although the processes and mechanisms driving hydrologic processes in dry and wet years are similar, their magnitude differs greatly. This study highlights that precipitation patterns affect the selection of optimal parameter values and the water budgets differed markedly between dry and wet years, as expected. The results of this study can assist urban watershed planners in selecting the most appropriate model to use for prediction purposes or when there is a paucity of data available for model calibration. This study recommends changing SWMM model parameters on an annual basis when projecting the effects of new conditions outside of current experience (i.e., land use and climate change) on urban runoff quantity and quality.

Biovalley analysis as a LID control to delay surface runoff in the city of Recife/PE

Urban growth brought with it the formation of large impermeable areas in cities, causing a reduction in the infiltration of rainwater into the soil and an increase in the volume of surface runoff, causing setbacks on days of extreme precipitation events. In this context, the present research aimed to analyze the use of a biotaver as a Low Impact Development (LID) for delaying surface runoff in areas prone to flooding in the city of Recife/PE. As a methodology, the QGIS model was used to extract data from raster images, and the Storm Water Management Model (SWMM) for scenario simulation. Three scenarios were simulated with rainfall of 20.00mm, 39.00mm and 60.00mm. The results showed that the implementation of the bioditches provided a reduction in the total flow of the study area by 0.50% and in the maximum flow of the SB1 sub-basin for scenarios 2 and 3 by 77.96% and 41.23% respectively. In addition, there was a reduction in surface runoff of 11.15% on average and an increase in green area of 4.00%. The bioretention system showed infiltration ranging from 29.83mm to 89.35mm between the three scenarios. Thus, through the SWMM model, it was possible to analyze that the implantation of bioditches as a control for delaying surface runoff and increasing infiltration into the soil is valid, and the literature recommended that various types of sustainable devices be implemented and integrated into the existing drainage network and urban space so that more expressive results are possible.

A novel ensemble algorithm based on hydrological event diversity for urban rainfall–runoff model calibration and validation

Hydrological models such as the stormwater management model (SWMM) are essential tools for managing water resources. Model performance typically relies on calibration to historic rainfall–runoff events. However, traditional calibration techniques suffer many issues, such as ad-hoc event selection, ignoring model parameter uncertainty, and poor validation techniques. We introduce a novel ensemble framework that relies on data-driven event selection of calibration and validation events to achieve robust model generalisation.The proposed framework is applied to SWMM models for two urbanised catchments in Southern Ontario, Canada. The framework uses unsupervised machine learning to cluster rainfall–runoff events. Events in different clusters have diverse characteristics; sampling events from each cluster ensures a diverse set of events for calibration. Validation events can be sampled in the same manner, which ensures similarity between calibration and validation data. We study the effects that the calibration event cluster has on model sensitivity and generalisation. Only minor differences in model parameter sensitivity were observed between different clusters. However, the cluster(s) used for calibration were found to be very important for model generalisation. Next, we generate ensemble predictions, which use multiple SWMM models, each calibrated to different events. Ensemble performance is compared to the single-model baseline and across increasing numbers of ensemble members. Results demonstrate improvements in generalisation and decrease in prediction variance across increasing ensemble size. Finally, we compare several different techniques for ensemble combination, which increase in complexity from simple averaging to ANN-based stacking augmented with exogenous input features. The ANN-based stacking performs the best according to two out of three evaluation criteria. Simple averaging, while inferior in performance to stacking, consistently outperforms the single model baseline. The novel framework proposed in this work provides a data-driven, adaptable alternative to conventional calibration frameworks that have been proven to improve model performance.

Development and Application of Urban Flood Prediction System for Smart Cities

To preemptively respond to urban flood disasters, a flood prediction system that provides a connection service with a smart city data hub was developed. The systems developed in this study provide real-time and predicted rainfall data, 1D and 2D flood prediction data, manhole-based flood prediction and warning information, and flood vulnerability index by administrative district. A one-dimensional analysis for the water depth and overflow amount of each manhole was calculated using the Storm Water Management Model (SWMM). A two-dimensional inundation analysis, the Two-Dimensional Inundation System (2DIS), which is capable of two-dimensional surface inundation analysis, was applied. The model can simulate surface flooding using the manhole overflow rate of the SWMM model. In the system, the flood prediction information was made based on the rainfall scenario, and the applied rainfall is 30-90 mm per hour, while the rainfall time distribution is Huff 3rd quartile. The propagation of flood conditions in the system can provide a five-stage (Safety, Attention, Caution, Alert, and Serious) scale depending on the water depth and surcharge conditions of the pipeline. The flood prediction model developed in this study was compared and verified with the existing flood survey data in the Dalseo Drainage Basin in 2002, and the model verification showed that the probability of flood detection (POD) was more than 80%.

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization is essential for reliable SWMM model simulation results, which are heterogeneously sensitive to a variety of parameters, especially when involving complicated simulation conditions. This study proposed a Genetic Algorithm-based parameter optimization method that combines the Morris screening method with the generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, the Morris screening method is used to determine the parameters for calibration, the GLUE method is employed to narrow down the range of parameter values, and the Genetic Algorithm is applied to further optimize the model parameters by considering objective constraints. The results show that the set of calibrated parameters, obtained by the integrated Morris and GLUE methods, can reduce the peak error by 9% for a simulation, and then the multi-objective constrained Genetic Algorithm reduces the model parameters’ peak error in the optimization process by up to 6%. During the validation process, the parameter set determined from the combination of both is used to obtain the optimal values of the parameters by the Genetic Algorithm. The proposed integrated method shows superior applicability for different rainfall intensities and rain-type events. These findings imply that the automated calibration of the SWMM model utilizing a Genetic Algorithm based on the combined parameter set of both has enhanced model simulation performance.

Urban storm flood simulation using improved SWMM based on K‐means clustering of parameter samples

AbstractTo address the two problems of unclear delineation of sub‐catchment and complicated and cumbersome parameter rate determination in the Storm Water Management Model (SWMM), this study proposes a rapid construction method of SWMM based on the principle of single urban functional area combined with K‐means clustering algorithm, The research area is the southern part of Jinshui District, Zhengzhou City. The Hydrological Response Unit (HRU) contains only a single urban functional area, divided by combining the natural and social attributes of the urban surface. Calibrated uncertain parameters from 76 papers were selected as samples, and the K‐means clustering algorithm was used to cluster and calculate the parameter values, to improve the SWMM model, selecting three typical rainfall runoff processes for validation application. The results show that simulated runoff is consistent with measured runoff trends, with the NSE and R2 value scores of the flow processes of the three floods above 0.86 and the, locations and numbers of flooded nodes are consistent with the actual research. This provides a new idea and technical support for the construction of urban flood models in flood prevention and mitigation. The relevant results can provide scientific decision‐making reference for urban flood forecasting and warning.