Notice: Undefined index: linkPowrot in C:\wwwroot\wwwroot\publikacje\publikacje.php on line 1275
Publikacje
Pomoc (F2)
[96560] Artykuł:

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

Czasopismo: Hydrology and Earth Sytem Sciences   Tom: 24, Zeszyt: 2, Strony: 595-614
ISSN:  1027-5606
Wydawca:  COPERNICUS GESELLSCHAFT MBH, BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
Opublikowano: Luty 2020
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3
Grupa
przynależności
Dyscyplina
naukowa
Procent
udziału
Liczba
punktów
do oceny pracownika
Liczba
punktów wg
kryteriów ewaluacji
Bartosz Szeląg orcid logo WiŚGiEKatedra Geotechniki, Geomatyki i Gospodarki Odpadami*Takzaliczony do "N"Inżynieria środowiska, górnictwo i energetyka25140.00140.00  
Roman Suligowski Niespoza "N" jednostki25.00.00  
Jan Studziński Niespoza "N" jednostki25.00.00  
Francesco De Paola Niespoza "N" jednostki25.00.00  

Grupa MNiSW:  Publikacja w czasopismach wymienionych w wykazie ministra MNiSzW (część A)
Punkty MNiSW: 140
Klasyfikacja Web of Science: Article


DOI LogoDOI     Web of Science Logo Web of Science    


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.