FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297267102> ?p ?o ?g. }

• W4297267102 abstract "Specific flood volume and degree of flooding are important parameters for evaluating the performance of stormwater networks. Hydrodynamic models are usually used to calculate these important measures, but this task requires the collection of detailed data on land use, the sewer network, rainfall, and flows, which are not always possible to obtain. The present research consists in the development of a methodology, using the USEPA Stormwater Management Model (SWMM), for simulating the performance of a stormwater network to determine whether it is in need of modernization. This determination is based on independent variables including rainfall data, catchment retention, and channel capacity. A logistic regression was developed to assess the sewer network performance based on simulation of specific flood volume and degree of flooding in the context of modernization. An extended sensitivity analysis was also used to assess the influence of rainfall intensity on the results of sensitivity coefficient calculations for the calibrated SWMM parameters. Using the extreme gradient boosting method, a tool has been developed to optimize the combination of SWMM parameters, reducing the uncertainty of simulation results, which can be used in the selection of their measurement methods prior to model development. It has been shown that, using the logistic regression model, it is possible to rapidly simulate the operation of a stormwater system to assess its need for modernization. It was confirmed that an increase in rainfall intensity leads to a significant decrease in the values of the calculated sensitivity coefficients associated with the SWMM parameters. The highest sensitivity coefficient was shown for a correction coefficient for percentage of impervious areas; for rainfall intensity 33–133 L·(s·ha)−1 varied from 1.45 to 12.38. This result leads to a method for selecting specific rainfall events for calibration of the model, thereby improving the ability to assess the performance of the stormwater system. Interestingly, however, for the exemplary catchment in Kielce, Poland, the generalized likelihood uncertainty estimation (GLUE) method was used, combined with the XGboost machine learning technique, to determine that the reliability of the SWMM parameters has a negligible impact on the probability of a stormwater network failure." @default.
• W4297267102 created "2022-09-28" @default.
• W4297267102 creator A5007376128 @default.
• W4297267102 creator A5009070681 @default.
• W4297267102 creator A5017672269 @default.
• W4297267102 creator A5018400831 @default.
• W4297267102 creator A5044283566 @default.
• W4297267102 creator A5050699096 @default.
• W4297267102 date "2022-12-01" @default.
• W4297267102 modified "2023-10-02" @default.
• W4297267102 title "Analysis of Sewer Network Performance in the Context of Modernization: Modeling, Sensitivity, and Uncertainty Analysis" @default.
• W4297267102 cites W1507963764 @default.
• W4297267102 cites W1955375869 @default.
• W4297267102 cites W2004429552 @default.
• W4297267102 cites W2052960370 @default.
• W4297267102 cites W2057143880 @default.
• W4297267102 cites W2059746554 @default.
• W4297267102 cites W2063404767 @default.
• W4297267102 cites W2081849888 @default.
• W4297267102 cites W2086276002 @default.
• W4297267102 cites W2090605318 @default.
• W4297267102 cites W2124738823 @default.
• W4297267102 cites W2126741760 @default.
• W4297267102 cites W2254972421 @default.
• W4297267102 cites W2289428875 @default.
• W4297267102 cites W2527310484 @default.
• W4297267102 cites W2528753301 @default.
• W4297267102 cites W2551054293 @default.
• W4297267102 cites W2770524937 @default.
• W4297267102 cites W2791721779 @default.
• W4297267102 cites W2800255409 @default.
• W4297267102 cites W2807638832 @default.
• W4297267102 cites W3025162817 @default.
• W4297267102 cites W3088480592 @default.
• W4297267102 cites W3097540554 @default.
• W4297267102 cites W3102476541 @default.
• W4297267102 cites W3144545015 @default.
• W4297267102 cites W3209831621 @default.
• W4297267102 doi "https://doi.org/10.1061/(asce)wr.1943-5452.0001610" @default.
• W4297267102 hasPublicationYear "2022" @default.
• W4297267102 type Work @default.
• W4297267102 citedByCount "5" @default.
• W4297267102 countsByYear W42972671022023 @default.
• W4297267102 crossrefType "journal-article" @default.
• W4297267102 hasAuthorship W4297267102A5007376128 @default.
• W4297267102 hasAuthorship W4297267102A5009070681 @default.
• W4297267102 hasAuthorship W4297267102A5017672269 @default.
• W4297267102 hasAuthorship W4297267102A5018400831 @default.
• W4297267102 hasAuthorship W4297267102A5044283566 @default.
• W4297267102 hasAuthorship W4297267102A5050699096 @default.
• W4297267102 hasConcept C127413603 @default.
• W4297267102 hasConcept C15744967 @default.
• W4297267102 hasConcept C166957645 @default.
• W4297267102 hasConcept C173051318 @default.
• W4297267102 hasConcept C186594467 @default.
• W4297267102 hasConcept C187320778 @default.
• W4297267102 hasConcept C18903297 @default.
• W4297267102 hasConcept C205649164 @default.
• W4297267102 hasConcept C21200559 @default.
• W4297267102 hasConcept C24326235 @default.
• W4297267102 hasConcept C2779343474 @default.
• W4297267102 hasConcept C39432304 @default.
• W4297267102 hasConcept C41008148 @default.
• W4297267102 hasConcept C50477045 @default.
• W4297267102 hasConcept C542102704 @default.
• W4297267102 hasConcept C76886044 @default.
• W4297267102 hasConcept C86803240 @default.
• W4297267102 hasConceptScore W4297267102C127413603 @default.
• W4297267102 hasConceptScore W4297267102C15744967 @default.
• W4297267102 hasConceptScore W4297267102C166957645 @default.
• W4297267102 hasConceptScore W4297267102C173051318 @default.
• W4297267102 hasConceptScore W4297267102C186594467 @default.
• W4297267102 hasConceptScore W4297267102C187320778 @default.
• W4297267102 hasConceptScore W4297267102C18903297 @default.
• W4297267102 hasConceptScore W4297267102C205649164 @default.
• W4297267102 hasConceptScore W4297267102C21200559 @default.
• W4297267102 hasConceptScore W4297267102C24326235 @default.
• W4297267102 hasConceptScore W4297267102C2779343474 @default.
• W4297267102 hasConceptScore W4297267102C39432304 @default.
• W4297267102 hasConceptScore W4297267102C41008148 @default.
• W4297267102 hasConceptScore W4297267102C50477045 @default.
• W4297267102 hasConceptScore W4297267102C542102704 @default.
• W4297267102 hasConceptScore W4297267102C76886044 @default.
• W4297267102 hasConceptScore W4297267102C86803240 @default.
• W4297267102 hasIssue "12" @default.
• W4297267102 hasLocation W42972671021 @default.
• W4297267102 hasOpenAccess W4297267102 @default.
• W4297267102 hasPrimaryLocation W42972671021 @default.
• W4297267102 hasRelatedWork W1606999027 @default.
• W4297267102 hasRelatedWork W2017469375 @default.
• W4297267102 hasRelatedWork W2026371027 @default.
• W4297267102 hasRelatedWork W2037581799 @default.
• W4297267102 hasRelatedWork W2180294781 @default.
• W4297267102 hasRelatedWork W2323070724 @default.
• W4297267102 hasRelatedWork W2432155914 @default.
• W4297267102 hasRelatedWork W3043623115 @default.
• W4297267102 hasRelatedWork W3147246668 @default.
• W4297267102 hasRelatedWork W3191175572 @default.
• W4297267102 hasVolume "148" @default.
• W4297267102 isParatext "false" @default.

• next