Generate_SWMM_inp: An Open-Source QGIS Plugin to Import and Export Model Input Files for SWMM

Schilling, Jannik; Tränckner, Jens

doi:10.3390/w14142262

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Generator tool uses the output generated from the Hydraulic Network Fixer tool to reorient the line segments so that the beginning and end nodes of each is consistently upstream and downstream, respectively, and identifies line segments immediately upstream and downstream of each line segment. Like the WaterNetAnalyzer plugin developed by Schilling (2021), this tool requires the specification of the outlet line segment. The outlet line segment is easily selected on the map canvas from the corrected network layer using the "Select Feature by Area or Single Click" tool in QGIS.…”

Section: (2): 337-348mentioning

confidence: 99%

“…The Network Flow Lengths tool was developed to determine the cumulative flow lengths for all connected adjoining line segments upstream to downstream in the tile layout network, similar to Nyberg et al (2015), Schilling (2021), and Zhang et al (2008). The tool uses the output from the Network Elevation Exports.…”

Section: Network Flow Lengthsmentioning

confidence: 99%

“…2a). Another 67(2): 337-348 plugin, the WaterNetAnalyzer (Schilling, 2021), can be used to create networks but does not fix the positional errors resulting from digitization. Significantly, QGIS includes a built-in plugin called 'Topology Checker,' which identifies and flags topology errors in a vector layer based on userspecified rules.…”

mentioning

confidence: 99%

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Development of Drainage Tools for Facilitating the Creation of topologically-Sound, Unidirectional Flow Tile Networks

Falasy,

Cooke

2024

Journal of the ASABE

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Highlights New publicly accessible drainage design QGIS plugins were developed. These tools were successfully used to create topologically-sound subsurface drainage layout networks. The tools are easy to use and reduce the level of expertise required to prepare drainage networks that can be successfully sized and buried. Abstract. An important step in designing a drainage system is laying out the network. Successful installation and sizing of the system require that the network be topologically sound and unidirectional. Online digitization of drainage systems does not always result in networks that meet these criteria. Tile lines may not be consistently digitized from upstream to downstream, and there may be disconnected segments, dangles, and overshoots. We have developed a set of open-source tools (plugins) in QGIS that convert a series of laterals and mains digitized on a map into a topologically-sound drainage network to facilitate both burying and sizing the drainage lines. Steps include reversing node numbering, if necessary, to ensure that tile nodes are numbered from upstream to downstream, connecting the ends of tile lines that are within a certain tolerance of each other, correcting overshoots, and setting an order for burying or calculating cumulative flow in line segments. An application example is presented using a proposed tile network layout at the university farm to demonstrate the capacity of these new tools to fix topologically disconnected tile lines and generate sound tile networks that can be successfully buried and sized. These easy-to-use tools are compatible with all versions of QGIS3 and may be freely downloaded or installed from the official QGIS Plugin Repository. Keywords: Line Geometry, Plugins, QGIS Software, Tile Layout Routines, Tile Nodes and Lines.

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Section: (2): 337-348mentioning

confidence: 99%

Section: Network Flow Lengthsmentioning

confidence: 99%

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Development of Drainage Tools for Facilitating the Creation of topologically-Sound, Unidirectional Flow Tile Networks

Falasy,

Cooke

2024

Journal of the ASABE

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“…SWMM operates through interconnected computational and service modules [5]. The computational simplicity and open-source nature of this model [6] have led to its widespread adoption. Its scope includes single and continuous rainfall events, conveyance processes within a pipe network, and pollutant accumulation and transport processes [7].…”

Section: Introductionmentioning

confidence: 99%

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

Gu,

Sun,

Gao

et al. 2024

Water

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In the context of accelerating urbanisation, the issue of urban stormwater flooding security has garnered increasing attention. Further development of urban stormwater management techniques is imperative to achieve more stable, precise, and expeditious simulation outcomes. The calibration of model parameters, which is a pivotal phase in stormwater simulation endeavours, is hampered by challenges such as substantial subjectivity, time intensiveness, and low efficiency. Therefore, this study introduces a parameter calibration model coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). This model utilises the Nash–Sutcliffe efficiency (NSE) and peak relative error (PE) values for various rainfall events as objective functions to calibrate and assess the study target. The two rainfalls used for rate determination had NSE values greater than 0.9 and absolute PE values less than 0.17; the rainfall used for validation had NSE values greater than 0.9 and absolute PE values less than 0.27. Thus, the results of the model for the rate determination of the parameters are reliable. In addition, the inverted generation distance and hypervolume values indicate that the iterative process of the algorithm during population evolution demonstrated stable iterative outcomes and ensured sound population quality. Both reach relative stability after 40 iterations. In conclusion, the proposed multi-objective parameter calibration model integrated with NSGA-III offers dependable calibration results and robust computational efficacy, presenting novel avenues and perspectives for urban stormwater model parameter calibration and simulation.

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Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

Jin,

Zhao,

et al. 2024

Int J Disaster Risk Sci

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This study presents a novel method for optimizing parameters in urban flood models, aiming to address the tedious and complex issues associated with parameter optimization. First, a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model. Next, a principle for dividing urban hydrological response units was introduced, incorporating surface attribute features. The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model, and an artificial neural network (ANN) was employed to identify the sensitive parameters. Finally, a genetic algorithm (GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model. The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient (NSE) of 0.81. Compared to the ANN-GA and K-means-deep neural networks (DNN) methods, the proposed method better characterizes the runoff generation and flow processes. This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

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Generate_SWMM_inp: An Open-Source QGIS Plugin to Import and Export Model Input Files for SWMM

Cited by 7 publications

References 22 publications

Development of Drainage Tools for Facilitating the Creation of topologically-Sound, Unidirectional Flow Tile Networks

Development of Drainage Tools for Facilitating the Creation of topologically-Sound, Unidirectional Flow Tile Networks

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

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