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The Characterization of Rainfall Data Set Using Persistence Diagram and Its Relation to Extreme Events: Case Study of Three Locations in Kemaman, Terengganu

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Soft Computing in Data Science (SCDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1771))

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Abstract

Floods are recurring phenomena at certain locations because of excessive rainfall, resulting in the overflow of lakes, drains, and rivers. In this work, we employ Persistence Homology (PH) to investigate the relationship between rainfall and flood that occurred from 1997 to 2018. Three stations in Kemaman, Terengganu, have been chosen to study this relationship. Persistence Diagram (PD) is one of the most powerful tools available under the umbrella of PH for detecting topological signatures in high dimension points cloud. In this paper, we use the rainfall time series dataset and express it in higher dimensions by selecting the embedded dimension, \(M = 5\), , and manipulating the time delay τ to obtain the maximum persistence. Then, we compared with past flood events which are labelled based on water level and PD’s max score to identify its suitability for flood identification. The area under the curve of receiver operation characteristics (ROC) have been used to measure the performance with three different thresholds for station 4131001, 4232002, and 4332001. The results clearly show PD’s significance to characterize the rainfall dataset as normal and flood events. The employed maximum persistence is robust despite missing data values.

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Acknowledgments

This work was supported by the Ministry of Education Malaysia grant FRGS/1/2019/STG06/UMT/02/2. The authors also acknowledge Department of Irrigation and Drainage Malaysia for providing the rainfall and water level data.

Author information

Authors and Affiliations

  1. Institute of Engineering Mathematics, University Malaysia Perlis, Arau, Perlis, Malaysia

    Z. A. Hasan

  2. Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia

    Z. A. Hasan & R. U. Gobithaasan

  3. Special Interest Group on Modelling and Data Analytics, Data and Digital Science Cluster, University Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia

    R. U. Gobithaasan

Authors
  1. Z. A. Hasan
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  2. R. U. Gobithaasan
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Corresponding author

Correspondence to R. U. Gobithaasan .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Malaysia

    Marina Yusoff

  2. Baoji University of Arts and Sciences, Baoji, China

    Tao Hai

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Murizah Kassim

  4. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  5. Institute of Liberal Arts and Sciences, Nagoya, Japan

    Eisuke Kita

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Hasan, Z.A., Gobithaasan, R.U. (2023). The Characterization of Rainfall Data Set Using Persistence Diagram and Its Relation to Extreme Events: Case Study of Three Locations in Kemaman, Terengganu. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_19

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  • DOI: https://doi.org/10.1007/978-981-99-0405-1_19

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  • Online ISBN: 978-981-99-0405-1

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