Predictive capacity of the mains and storm water chain in real time as a SaaS service based on data obtained from machine-learning. (Q77717): Difference between revisions
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(Removed claim: summary (P836): Climate change is an ongoing process whose effects are visible on a global scale.In many areas there has been a significant increase in the intensity of rainfall and high rainfall is more likely to occur even in areas with reduced overall precipitation.The evolution of the natural hydrological cycle of the human activity is caused by human activities.This affects the increase in the risk of flooding.In order to properly manage the situation of...) |
(Created claim: summary (P836): Climate change is a progressive process, the effects of which are visible globally. In many areas precipitation has increased significantly, and heavy rains occur even in areas with reduced total rainfall. The course of the natural hydrological cycle is disturbed by human activity. This increases the risk of flooding. In order to manage the flood situation properly, it is necessary to predict the efficiency of the rainwater sewer system and full...) |
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Climate change is a progressive process, the effects of which are visible globally. In many areas precipitation has increased significantly, and heavy rains occur even in areas with reduced total rainfall. The course of the natural hydrological cycle is disturbed by human activity. This increases the risk of flooding. In order to manage the flood situation properly, it is necessary to predict the efficiency of the rainwater sewer system and full information about the actual threat. At present, engineers are making tedious analyses to determine whether the current network infrastructure will ensure adequate drainage of rainwater. However, hydrological analyses are burdened with a number of defects: they take many days, require the involvement of a whole team of people who have limited network overview, do not take into account the variability of the surveyed network over time, are costly, rely on data burdened with errors and do not provide real-time predictability, which means that cities and/or water and/or water and sewage companies can only monitor and report the state of the infrastructure rather than respond effectively to potential threats in advance. Taking this into account, it is necessary to develop and implement a system to predict the level of liquids in the storm drain pipe on the basis of measurement data from different network points and data on expected precipitation affecting the network. The project focuses on industrial research and experimental development, resulting in the creation of an internationally innovative software in the SaaS model. System as a Service, Pol. Software as a Service). (English) | |||||||||||||||
| Property / summary: Climate change is a progressive process, the effects of which are visible globally. In many areas precipitation has increased significantly, and heavy rains occur even in areas with reduced total rainfall. The course of the natural hydrological cycle is disturbed by human activity. This increases the risk of flooding. In order to manage the flood situation properly, it is necessary to predict the efficiency of the rainwater sewer system and full information about the actual threat. At present, engineers are making tedious analyses to determine whether the current network infrastructure will ensure adequate drainage of rainwater. However, hydrological analyses are burdened with a number of defects: they take many days, require the involvement of a whole team of people who have limited network overview, do not take into account the variability of the surveyed network over time, are costly, rely on data burdened with errors and do not provide real-time predictability, which means that cities and/or water and/or water and sewage companies can only monitor and report the state of the infrastructure rather than respond effectively to potential threats in advance. Taking this into account, it is necessary to develop and implement a system to predict the level of liquids in the storm drain pipe on the basis of measurement data from different network points and data on expected precipitation affecting the network. The project focuses on industrial research and experimental development, resulting in the creation of an internationally innovative software in the SaaS model. System as a Service, Pol. Software as a Service). (English) / rank | |||||||||||||||
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| Property / summary: Climate change is a progressive process, the effects of which are visible globally. In many areas precipitation has increased significantly, and heavy rains occur even in areas with reduced total rainfall. The course of the natural hydrological cycle is disturbed by human activity. This increases the risk of flooding. In order to manage the flood situation properly, it is necessary to predict the efficiency of the rainwater sewer system and full information about the actual threat. At present, engineers are making tedious analyses to determine whether the current network infrastructure will ensure adequate drainage of rainwater. However, hydrological analyses are burdened with a number of defects: they take many days, require the involvement of a whole team of people who have limited network overview, do not take into account the variability of the surveyed network over time, are costly, rely on data burdened with errors and do not provide real-time predictability, which means that cities and/or water and/or water and sewage companies can only monitor and report the state of the infrastructure rather than respond effectively to potential threats in advance. Taking this into account, it is necessary to develop and implement a system to predict the level of liquids in the storm drain pipe on the basis of measurement data from different network points and data on expected precipitation affecting the network. The project focuses on industrial research and experimental development, resulting in the creation of an internationally innovative software in the SaaS model. System as a Service, Pol. Software as a Service). (English) / qualifier | |||||||||||||||
point in time: 14 October 2020
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Revision as of 10:05, 14 October 2020
Project in Poland financed by DG Regio
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Predictive capacity of the mains and storm water chain in real time as a SaaS service based on data obtained from machine-learning. |
Project in Poland financed by DG Regio |
Statements
1,157,827.32 zloty
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1,499,998.5 zloty
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77.19 percent
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1 September 2017
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31 October 2018
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CARL DATA SOLUTIONS PL SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
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54°21'41.0"N, 18°37'41.5"E
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Zmiany klimatu to postępujący proces, którego skutki są widoczne w skali globalnej. Na wielu obszarach znacząco zwiększyła się intensywność opadów, a deszcze o dużym natężeniu częściej występują nawet na terenach o zmniejszonej całkowitej sumie opadów. Przebieg naturalnego cyklu hydrologicznego zaburzany jest przez działalność człowieka. Wpływa to na wzrost wystąpienia ryzyka powodzi. W celu właściwego zarządzania sytuacją powodziową niezbędna jest możliwość predykcji wydajności sieci kanalizacji deszczowej oraz pełna informacja o rzeczywistym aktualnym zagrożeniu. Obecnie inżynierowie wykonują żmudne analizy w celu określenia czy obecna infrastruktura majątku sieciowego zagwarantuje odpowiednie odprowadzenie wody deszczowej. Analizy hydrologów obarczone są jednak szeregiem wad: zajmują wiele dni, wymagają zaangażowania całego zespołu osób, które mają ograniczony ogląd sieci, nie uwzględniają zmienności badanej sieci w czasie, są kosztowne, bazują na danych obarczonych błędami oraz nie dają możliwości przewidywania zdarzeń w czasie rzeczywistym, co powoduje, że miasta i/lub przedsiębiorstwa wodno-kanalizacyjne mogą tylko monitorować i raportować stan infrastruktury, a nie skutecznie reagować na potencjalne zagrożenia z wyprzedzeniem. Biorąc pod uwagę powyższe występuje konieczność opracowania i wdrożenia systemu umożliwiającego predykcję poziomu cieczy w rurze kanalizacyjno-burzowej na podstawie danych pomiarowych pochodzących z różnych punktów sieci oraz danych dotyczących wielkości przewidywanych opadów oddziaływujących na daną sieć. Przedmiotem projektu są badania przemysłowe i eksperymentalne prace rozwojowe rezultatem których jest stworzenie innowacyjnego w skali międzynarodowej oprogramowania w modelu SaaS (ang. System as a Service, pol. Oprogramowanie jako Usługa). (Polish)
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Climate change is a progressive process, the effects of which are visible globally. In many areas precipitation has increased significantly, and heavy rains occur even in areas with reduced total rainfall. The course of the natural hydrological cycle is disturbed by human activity. This increases the risk of flooding. In order to manage the flood situation properly, it is necessary to predict the efficiency of the rainwater sewer system and full information about the actual threat. At present, engineers are making tedious analyses to determine whether the current network infrastructure will ensure adequate drainage of rainwater. However, hydrological analyses are burdened with a number of defects: they take many days, require the involvement of a whole team of people who have limited network overview, do not take into account the variability of the surveyed network over time, are costly, rely on data burdened with errors and do not provide real-time predictability, which means that cities and/or water and/or water and sewage companies can only monitor and report the state of the infrastructure rather than respond effectively to potential threats in advance. Taking this into account, it is necessary to develop and implement a system to predict the level of liquids in the storm drain pipe on the basis of measurement data from different network points and data on expected precipitation affecting the network. The project focuses on industrial research and experimental development, resulting in the creation of an internationally innovative software in the SaaS model. System as a Service, Pol. Software as a Service). (English)
14 October 2020
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Identifiers
POIR.01.01.01-00-0134/17
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