Smart Sewage Water Management and Data Forecast

Qinghua Wang; Viktor Westlund; Jonas Johansson; Magnus Lindgren

doi:10.1109/SAIS53221.2021.9484017

Sammanfattning

There is currently an ongoing digital transformation for sewage and wastewater management. By automating data collection and enabling remote monitoring, we will not only be able to save abundant human resources but also enabling predictive maintenance which is based on big data analytics. This paper presents a smart sewage water management system which is currently under development in southern Sweden. Real-time data can be collected from over 500 sensors which have already been partially deployed. Preliminary data analysis shows that we can build statistical data models for ground water, rainfall, and sewage water flows, and use those models for data forecast and anomaly detection.

Originalspråk	Engelska
DOI	https://doi.org/10.1109/SAIS53221.2021.9484017
Status	Publicerad - 2021
Evenemang	2021 Swedish Artificial Intelligence Society Workshop (SAIS), Luleå/Hybrid: Virtual, online, June 14-15, 2021 - Varaktighet: 1980-jan.-01 → …

Konferens

Konferens	2021 Swedish Artificial Intelligence Society Workshop (SAIS), Luleå/Hybrid: Virtual, online, June 14-15, 2021
Period	80-01-01 → …

Nationell ämneskategori

Datavetenskap (10201)
Datorsystem (20206)
Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning (50804)

Åtkomst av dokument

10.1109/SAIS53221.2021.9484017

Machine learning model design for IoT-based flooding forecast
Wang, Q., 2022, s. 97-103. 7 s.
Forskningsoutput: Konferensbidrag › Paper › Peer review
2 Citeringar (Scopus)

Smart-VA: Realtidsövervakat smart VA-system för uppmätt breddning med live rapportering till kommuninvånarna
Wang, Q. (PI), Mårtensson, L. (Col), Abdelrahman, W. (Forskare), Veletanlic, B. (Administratör), Frisk, F. (Forskare), Johansson, O. (Forskare), Lindgren, M. (Forskare) & Wemmenborn, M. (Projektledare)
21-01-15 → 23-01-14
Projekt: Forskning

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@conference{e53ae84e277f4805af8a9546da99afd9,

title = "Smart Sewage Water Management and Data Forecast",

abstract = "There is currently an ongoing digital transformation for sewage and wastewater management. By automating data collection and enabling remote monitoring, we will not only be able to save abundant human resources but also enabling predictive maintenance which is based on big data analytics. This paper presents a smart sewage water management system which is currently under development in southern Sweden. Real-time data can be collected from over 500 sensors which have already been partially deployed. Preliminary data analysis shows that we can build statistical data models for ground water, rainfall, and sewage water flows, and use those models for data forecast and anomaly detection.",

keywords = "anomaly detection, big data, internet of things, sewage water management",

author = "Qinghua Wang and Viktor Westlund and Jonas Johansson and Magnus Lindgren",

note = "Funding Information: *This work is supported by the strategic innovation programme IoT Sweden, which is a joint effort by Vinnova, the Swedish research council Formas and the Swedish Energy Agency. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 Swedish Artificial Intelligence Society Workshop (SAIS), Lule{\aa}/Hybrid: Virtual, online, June 14-15, 2021 ; Conference date: 01-01-1980",

year = "2021",

doi = "10.1109/SAIS53221.2021.9484017",

language = "English",

}

TY - CONF

T1 - Smart Sewage Water Management and Data Forecast

AU - Wang, Qinghua

AU - Westlund, Viktor

AU - Johansson, Jonas

AU - Lindgren, Magnus

N1 - Funding Information: *This work is supported by the strategic innovation programme IoT Sweden, which is a joint effort by Vinnova, the Swedish research council Formas and the Swedish Energy Agency. Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - There is currently an ongoing digital transformation for sewage and wastewater management. By automating data collection and enabling remote monitoring, we will not only be able to save abundant human resources but also enabling predictive maintenance which is based on big data analytics. This paper presents a smart sewage water management system which is currently under development in southern Sweden. Real-time data can be collected from over 500 sensors which have already been partially deployed. Preliminary data analysis shows that we can build statistical data models for ground water, rainfall, and sewage water flows, and use those models for data forecast and anomaly detection.

AB - There is currently an ongoing digital transformation for sewage and wastewater management. By automating data collection and enabling remote monitoring, we will not only be able to save abundant human resources but also enabling predictive maintenance which is based on big data analytics. This paper presents a smart sewage water management system which is currently under development in southern Sweden. Real-time data can be collected from over 500 sensors which have already been partially deployed. Preliminary data analysis shows that we can build statistical data models for ground water, rainfall, and sewage water flows, and use those models for data forecast and anomaly detection.

KW - anomaly detection

KW - big data

KW - internet of things

KW - sewage water management

U2 - 10.1109/SAIS53221.2021.9484017

DO - 10.1109/SAIS53221.2021.9484017

M3 - Abstract

T2 - 2021 Swedish Artificial Intelligence Society Workshop (SAIS), Luleå/Hybrid: Virtual, online, June 14-15, 2021

Y2 - 1 January 1980

ER -

Smart Sewage Water Management and Data Forecast

Sammanfattning

Konferens

Nationell ämneskategori

Åtkomst av dokument

Fingeravtryck

Forskningsoutput

Machine learning model design for IoT-based flooding forecast

Projekt

Smart-VA: Realtidsövervakat smart VA-system för uppmätt breddning med live rapportering till kommuninvånarna

Citera det här