Streamflow data analysis for flood detection using persistent homology
Flooding is an environmental hazard that occurs almost everywhere around the world. Analysis of streamflow data can give us important climatic information for flooding events. Persistent homology (PH), a new analysis tool in topological data analysis (TDA) offers a new way to look at the informati...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20249/1/22.pdf http://journalarticle.ukm.my/20249/ https://www.ukm.my/jsm/malay_journals/jilid51bil7_2022/KandunganJilid51Bil7_2022.html |
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| Summary: | Flooding is an environmental hazard that occurs almost everywhere around the world. Analysis of streamflow data can
give us important climatic information for flooding events. Persistent homology (PH), a new analysis tool in topological
data analysis (TDA) offers a new way to look at the information in a data set using qualitative approach. PH uses
topology to extract topological features such as connected components and cycles that exist in the data set. In this paper,
we present a new approach for streamflow data analysis for flood detection by using PH. An analysis was conducted
at Sungai Kelantan, Malaysia. The result shows that PH gives different pattern of topological features for dry and wet
periods. In particular, there are more persistent topological features in the form of connected components and cycles in
the wet periods compared to the dry periods. We observed that the time series of the distance measure corresponding
to the evolution of the components is consistent with the time series of the streamflow data. As a conclusion, this study
suggests that the time series of the distance measure corresponding to the evolution of the components can be used for
flood detection at Sungai Kelantan, Malaysia. |
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