An innovative method of predicting the maximum flow in stormwater sewage systems using soft-sensors

• Autorzy: Barbusiński Krzysztof , Szeląg Bartosz , Białek Anita , Kalenik Marek , Bakalar Tomas

Identyfikatory

DOI

10.24425/aep.2025.156009

Warianty tytułu

PL

Innowacyjna metoda prognozowania maksymalnego przepływu w systemach kanalizacji deszczowej z wykorzystaniem soft-sensorów

• Języki publikacji: EN

Abstrakty

EN

Developing universal hydrological models for modeling urban catchments remains one of the major challenges in contemporary hydrology. This study aimed to create a model that integrates catchment characteristics, sewer network topology, sewer storage capacity, and rainfall data, along with a sensitivity analysis of input parameters. The goal was to evaluate the potential of advanced analytical methods, specifically, Multivariate Adaptive Regression Splines (MARS) and soft-sensor technology, to improve peak flow (Qm) forecasting in stormwater systems. The results showed that combining MARS models with soft sensors yields high forecasting accuracy (R² = 0.96, RMSE = 0.038), even under variable rainfall conditions. However, the development of universally applicable model relationships proved challenging due to difficulties in parameterizing the model under changing rainfall scenarios. Additionally, the inclusion of a risk analysis method also enabled consideration of sewer network capacity and introduced a safety margin coefficient to assess system flexibility under future climate conditions. While the proposed approach does not lead to the creation of universal tools, it offers valuable insights for further research on adapting sewer systems to evolving hydrological conditions. The findings suggest promising directions for the development of cost-free, zero-emission soft sensors and models adaptable across diverse urban catchments.

PL

Tworzenie uniwersalnych modeli hydrologicznych do modelowania zlewni miejskich jest jednym z największych wyzwań współczesnej hydrologii. Niniejsze badanie podejmuje próbę opracowania modelu uwzględniającego różnorodne cechy zlewni, topologię sieci kanalizacyjnej, retencję w kanałach oraz dane opadowe, a także przeprowadzenia analizy wrażliwości modelu na zmiany parametrów wejściowych. Celem było zbadanie, w jakim stopniu zastosowanie zaawansowanych metod analitycznych, takich jak modele MARS (Multivariate Adaptive Regression Splines) oraz technologia soft-sensorów, może poprawić prognozowanie przepływów szczytowych (Qm) w systemach kanalizacji deszczowej. W badaniu wykorzystano zaawansowane metody analityczne, w tym modele MARS, do prognozowania przepływów szczytowych w systemach kanalizacji deszczowej. W proces modelowania włączono technologię soft-sensorów, uwzględniającą różnorodne cechy zlewni, topologię sieci kanalizacyjnej, retencję w kanałach oraz zmienne dane opadowe. Dodatkowo przeprowadzono analizę wrażliwości w celu oceny reakcji modelu na zmiany parametrów wejściowych. Wyniki pokazały, że połączenie modeli MARS z soft-sensorami pozwala na uzyskanie wysokiej dokładności prognoz (R² = 0,96, RMSE = 0,038), pomimo zmienności warunków opadowych. Jednakże, nie udało się opracować uniwersalnych zależności modelowych z powodu trudności w parametryzacji modelu w kontekście zmiennych danych opadowych. Chociaż proponowane podejście nie prowadzi do stworzenia uniwersalnych narzędzi, dostarcza cennych wskazówek do dalszych badań nad adaptacją systemów kanalizacyjnych do zmieniających się warunków hydrologicznych. Zastosowana metoda analizy ryzyka umożliwiła uwzględnienie przepustowości sieci kanalizacyjnej oraz wprowadzenie współczynnika marginesu bezpieczeństwa, który ocenia elastyczność systemu w kontekście przyszłych zmian klimatycznych. Badanie wskazuje kierunki przyszłych prac nad modelami, które mogłyby być stosowane w różnych zlewniach miejskich, szczególnie poprzez rozwój bezkosztowych, zeroemisyjnych soft-sensorów.

Słowa kluczowe

EN

model uncertainty; soft sensors; maximum flow prediction; machine learning - ML; energy consumption optimization; MARS model

PL

prognozowanie przepływów szczytowych; model MARS; metoda analizy ryzyka; soft-sensory

• Wydawca: Institute of Environmental Engineering, Polish Academy of Sciences
• Czasopismo: Archives of Environmental Protection
• Rocznik: 2025
• Tom: Vol. 51, no. 3
• Strony: 54--73
• Opis fizyczny: Bibliogr. 83 poz., rys., tab., wykr.

Twórcy

autor

Barbusiński Krzysztof

• Department of Water and Wastewater Engineering, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0001-8718-6626

autor

Szeląg Bartosz

• Faculty of Environmental Engineering, Geomatics and Renewable Energy, Kielce University of Technology, Poland

• https://orcid.org/0000-0002-0559-5475

autor

Białek Anita

• Faculty of Environmental Engineering, Geomatics and Renewable Energy, Kielce University of Technology, Poland

• https://orcid.org/0000-0002-3462-3877

autor

Kalenik Marek

• Institute of Environmental Engineering, Warsaw University of Life Sciences-SGGW, Poland

• https://orcid.org/0000-0001-6184-1899

autor

Bakalar Tomas

• Institute of Earth Resources, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Slovak Republic

• https://orcid.org/0000-0002-6985-9935

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).