ABSTRACT:

Effective hydrologic-hydraulic model development such as U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) depends on the data availability and data completeness of as-built stormwater infrastructure data. The infrastructure data gaps affect accurate process representation in model causing output uncertainty, error and bias, which further affect model construction, parameterization and its reliable use. However, complete stormwater infrastructure data are often not available due to data sharing restrictions or data gaps occurring from errors of omission (i.e., infrastructure components not being recorded) and error of commission (i.e., assignment of incorrect data). This algorithm, created in R, fills the missing stormwater infrastructure attribute-values data in accordance with the available design standards and modeling practice. It can be adopted to fill missing stormwater infrastructure attributes data for any size of SWMM model. This algorithm can also be implemented to randomly sample, using Monte Carlo sampling approach, the effects of missing attribute-values for different parameters of conduits and junctions such as diameter, roughness and depth.

For details about this work readers are referred to:

1). Shrestha, A., Mascaro, G., & Garcia, M. (2022). Effects of stormwater infrastructure data completeness and model resolution on urban flood modeling. Journal of Hydrology, 607, 127498. https://doi.org/10.1016/j.jhydrol.2022.127498
2). Shrestha, A. (2022). Advances in Urban Flood Management: Addressing Data Uncertainty, Data Gaps and Adaptation Planning (Doctoral dissertation, Arizona State University). https://search.proquest.com/openview/b79c1eb133e93ea0a07b6147fe7feff6/1?pq-origsite=gscholar&cbl=18750&diss=y

For GitHub link to this repository, readers are referred to:
1). https://github.com/ashish-shrs/filling_missing_data_for_swmm/tree/main

ABSTRACT:

The first part of this repository includes a Python file containing two functions that utilize ESRI's ArcGIS arcpy library. Users can input shapefiles (polygons) of sub-catchments and raster files of land use land cover, and soil types. Additionally, another function allows users to input shapefiles (polylines) of the stormwater network, which include data on built material types and the age of infrastructure, to generate grouped categories of sub-catchments and stormwater conduits.

The second part of the repository contains an R file, which includes two algorithms. The first algorithm extracts time series data of nodes' flooding from a one-dimensional SWMM model and overland flood water depth from a one- and two-dimensional coupled version of the SWMM model. It then establishes a statistical relationship between the two models. The second algorithm parameterizes the SWMM 1D version using a "Genetic Algorithm" for single objective optimization in parallel computing nodes.

For details about this work readers are referred to:

1). Shrestha, A., Garcia, M. & Doerry, E. (2024). Leveraging catchment scale automated novel data collection infrastructure to advance urban hydrologic-hydraulic modeling. Environmental Modelling & Software.
https://doi.org/10.1016/j.envsoft.2024.106046
2). Shrestha, A. (2022). Advances in Urban Flood Management: Addressing Data Uncertainty, Data Gaps and Adaptation Planning (Doctoral dissertation, Arizona State University). https://search.proquest.com/openview/b79c1eb133e93ea0a07b6147fe7feff6/1?pq-origsite=gscholar&cbl=18750&diss=y

For GitHub links to this repository, and any updates, readers are referred to:
1). https://github.com/ashish-shrs/Parameter_grouping_for_hydrologic-hydraulic_model_calibration
2). https://github.com/ashish-shrs/Algorithm_for_novel_data_application_in_hydrologic-hydraulic_model_calibration