Machine learning model design for IoT-based flooding forecast

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

Flooding risk is a threat to sea-level residential areas in southern Sweden. An Internet of things (IoT) project has been deployed to monitor weather and water pipe conditions in Kristianstad, Sweden. The IoT data however only monitors the current condition and does not tell the future threat. Machine learning models using deep learning neural networks have been developed to predict future threats based on IoT data and weather forecast. This paper presents multiple model architectures and their performances. All the models are explainable. Finally, a conclusion is made by selecting the best-functioning model in the context of flooding risk prediction in Kristianstad.
Original languageEnglish
Pages97-103
Number of pages7
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery (CyberC) - Jiangsu, China, Suzhou, China
Duration: 2022-Dec-152022-Dec-16
Conference number: 2022
https://conferences.computer.org/cybercpub/#!/toc/0

Conference

Conference2022 International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery (CyberC)
Abbreviated titleCyberC
Country/TerritoryChina
CitySuzhou
Period22-12-1522-12-16
Internet address

Swedish Standard Keywords

  • Electrical Engineering, Electronic Engineering, Information Engineering (202)

Keywords

  • Flooding risk prediction
  • Time series forecast
  • Explainable AI
  • Neural network

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