Zabidi Abu Hasan
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Preferred name
Zabidi Abu Hasan
Official Name
Zabidi, Abu Hasan
Alternative Name
Abu Hasan, Zabidi
Abu Hasan, Z.
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Scopus Author ID
56288336400
Researcher ID
ELE-8774-2022

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  • Publication
    The Characterization of Rainfall Data Set Using Persistence Diagram and Its Relation to Extreme Events: Case Study of Three Locations in Kemaman, Terengganu
    ( 2023-01-01)
    Zabidi Abu Hasan
    ;
    Gobithaasan R.U.
    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.
  • Publication
    Identifying Irregular Rainfall Patterns Using Persistent Homology
    ( 2024-05-01)
    Zabidi Abu Hasan
    ;
    Gobithaasan R.U.
    ;
    Mohd Zulkepli N.F.S.
    ;
    Ali M.Z.M.
    ;
    Miura K.T.
    ;
    DÅ‚otko P.
    Efficient investigation tools are required to elucidate the changes in climatic change caused by various climate processes, variables, and socioeconomic development activities around the world. In this study, we track the changes of daily rainfall at three flood-prone sites in Terengganu between 2012 to 2017. In recent years, topological data analysis (TDA) has been applied in many fields of data analytics to rank, classify, and cluster time series datasets. In this work, we employ Persistent Homology to quantify and identify topological patterns from a rainfall data. A sliding window (SW) approach is used for each 1D rainfall dataset to embed in higher dimensions before computing its Persistence Diagrams (PD). The topological information obtained from PD, namely connected components (H0) is then retrieved and vectorized in the form of Persistence Curves (Persistence Landscape (PL), Persistence lifetime Curve (PLC), and Persistence Lifetime Entropy (PLE)) to identify unusual rainfall patterns. We employ various types of L1-norms from these Persistence vectors to identify anomalies in rainfall data which can be used as an early warning flood system. The irregular pattern of Persistence lifetime and Persistence entropy mismatch the actual flood events suggesting that the irregular points may not be as closely related to flood risk. However, PL analysis of the irregular points shows match of about 59% to the flood events. It is expected that other determining factors, for example, land use, cloud cover, and wind information, which can be obtained via satellite gridded data may increase the predictability of flood events thus promotes an effective flood risk management strategies.
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