FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2964981625> ?p ?o ?g. }

• W2964981625 endingPage "614" @default.
• W2964981625 startingPage "595" @default.
• W2964981625 abstract "Abstract. One of the key parameters constituting the basis for the operational assessment of stormwater systems is the annual number of storm overflows. Since uncontrolled overflows are a source of pollution washed away from the surface of the catchment area, which leads to imbalanced receiving waters, there is a need for their prognosis and potential reduction. The paper presents a probabilistic model for simulating the annual number of storm overflows. In this model, an innovative solution is to use the logistic regression method to analyze the impact of rainfall genesis on the functioning of a storm overflow (OV) in the example of a catchment located in the city of Kielce (central Poland). The developed model consists of two independent elements. The first element of the model is a synthetic precipitation generator, in which the simulation of rainfall takes into account its genesis resulting from various processes and phenomena occurring in the troposphere. This approach makes it possible to account for the stochastic nature of rainfall in relation to the annual number of events. The second element is the model of logistic regression, which can be used to model the storm overflow resulting from the occurrence of a single rainfall event. The paper confirmed that storm overflow can be modeled based on data on the total rainfall and its duration. An alternative approach was also proposed, providing the possibility of predicting storm overflow only based on the average rainfall intensity. Substantial simplification in the simulation of the phenomenon under study was achieved compared with the works published in this area to date. It is worth noting that the coefficients determined in the logit models have a physical interpretation, and the universal character of these models facilitates their easy adaptation to other examined catchment areas. The calculations made in the paper using the example of the examined catchment allowed for an assessment of the influence of rainfall characteristics (depth, intensity, and duration) of different genesis on the probability of storm overflow. Based on the obtained results, the range of the variability of the average rainfall intensity, which determines the storm overflow, and the annual number of overflows resulting from the occurrence of rain of different genesis were defined. The results are suited for the implementation in the assessment of storm overflows only based on the genetic type of rainfall. The results may be used to develop warning systems in which information about the predicted rainfall genesis is an element of the assessment of the rainwater system and its facilities. This approach is an original solution that has not yet been considered by other researchers. On the other hand, it represents an important simplification and an opportunity to reduce the amount of data to be measured." @default.
• W2964981625 created "2019-08-13" @default.
• W2964981625 creator A5007376128 @default.
• W2964981625 creator A5012279224 @default.
• W2964981625 creator A5068568805 @default.
• W2964981625 creator A5091631186 @default.
• W2964981625 date "2020-02-12" @default.
• W2964981625 modified "2023-10-10" @default.
• W2964981625 title "Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach" @default.
• W2964981625 cites W1498867764 @default.
• W2964981625 cites W1527206100 @default.
• W2964981625 cites W1533344547 @default.
• W2964981625 cites W1602443498 @default.
• W2964981625 cites W1891455164 @default.
• W2964981625 cites W1919296332 @default.
• W2964981625 cites W1955375869 @default.
• W2964981625 cites W1982943667 @default.
• W2964981625 cites W1994665973 @default.
• W2964981625 cites W1995855907 @default.
• W2964981625 cites W1998038356 @default.
• W2964981625 cites W2006114442 @default.
• W2964981625 cites W2006180217 @default.
• W2964981625 cites W2006441910 @default.
• W2964981625 cites W2013089794 @default.
• W2964981625 cites W2021035653 @default.
• W2964981625 cites W2021071264 @default.
• W2964981625 cites W2021971489 @default.
• W2964981625 cites W2023977217 @default.
• W2964981625 cites W2024689205 @default.
• W2964981625 cites W2024789740 @default.
• W2964981625 cites W2055862320 @default.
• W2964981625 cites W2057143880 @default.
• W2964981625 cites W2060107661 @default.
• W2964981625 cites W2083021975 @default.
• W2964981625 cites W2090605318 @default.
• W2964981625 cites W2094212524 @default.
• W2964981625 cites W2100093956 @default.
• W2964981625 cites W2101007820 @default.
• W2964981625 cites W2101448662 @default.
• W2964981625 cites W2101462028 @default.
• W2964981625 cites W2109021952 @default.
• W2964981625 cites W2111410742 @default.
• W2964981625 cites W2116699155 @default.
• W2964981625 cites W2122344251 @default.
• W2964981625 cites W2122588877 @default.
• W2964981625 cites W2124979731 @default.
• W2964981625 cites W2126741760 @default.
• W2964981625 cites W2133398539 @default.
• W2964981625 cites W2133519869 @default.
• W2964981625 cites W2137860845 @default.
• W2964981625 cites W2158432004 @default.
• W2964981625 cites W2167737647 @default.
• W2964981625 cites W2174870528 @default.
• W2964981625 cites W2809985996 @default.
• W2964981625 cites W4247816226 @default.
• W2964981625 doi "https://doi.org/10.5194/hess-24-595-2020" @default.
• W2964981625 hasPublicationYear "2020" @default.
• W2964981625 type Work @default.
• W2964981625 sameAs 2964981625 @default.
• W2964981625 citedByCount "12" @default.
• W2964981625 countsByYear W29649816252021 @default.
• W2964981625 countsByYear W29649816252022 @default.
• W2964981625 countsByYear W29649816252023 @default.
• W2964981625 crossrefType "journal-article" @default.
• W2964981625 hasAuthorship W2964981625A5007376128 @default.
• W2964981625 hasAuthorship W2964981625A5012279224 @default.
• W2964981625 hasAuthorship W2964981625A5068568805 @default.
• W2964981625 hasAuthorship W2964981625A5091631186 @default.
• W2964981625 hasBestOaLocation W29649816251 @default.
• W2964981625 hasConcept C105306849 @default.
• W2964981625 hasConcept C105795698 @default.
• W2964981625 hasConcept C107054158 @default.
• W2964981625 hasConcept C126645576 @default.
• W2964981625 hasConcept C127313418 @default.
• W2964981625 hasConcept C153294291 @default.
• W2964981625 hasConcept C173051318 @default.
• W2964981625 hasConcept C187320778 @default.
• W2964981625 hasConcept C18903297 @default.
• W2964981625 hasConcept C205649164 @default.
• W2964981625 hasConcept C33923547 @default.
• W2964981625 hasConcept C39432304 @default.
• W2964981625 hasConcept C49204034 @default.
• W2964981625 hasConcept C49937458 @default.
• W2964981625 hasConcept C50477045 @default.
• W2964981625 hasConcept C58640448 @default.
• W2964981625 hasConcept C76886044 @default.
• W2964981625 hasConcept C86803240 @default.
• W2964981625 hasConceptScore W2964981625C105306849 @default.
• W2964981625 hasConceptScore W2964981625C105795698 @default.
• W2964981625 hasConceptScore W2964981625C107054158 @default.
• W2964981625 hasConceptScore W2964981625C126645576 @default.
• W2964981625 hasConceptScore W2964981625C127313418 @default.
• W2964981625 hasConceptScore W2964981625C153294291 @default.
• W2964981625 hasConceptScore W2964981625C173051318 @default.
• W2964981625 hasConceptScore W2964981625C187320778 @default.
• W2964981625 hasConceptScore W2964981625C18903297 @default.
• W2964981625 hasConceptScore W2964981625C205649164 @default.
• W2964981625 hasConceptScore W2964981625C33923547 @default.

• next