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

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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

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.

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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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Clustering selected Terengganu’s rainfall stations based on persistent homology

2022-01-01 , Gobithaasan R.U. , Zabidi Abu Hasan , Selvarajh K.D. , Wong K.S. , Mamat S. , Ali M.Z.M. , Miura K.T. , Dotko P.

Topological Data Analysis (TDA) is an emerging technique rooted from Algebraic Topology that reveals the geometrical structure of high-dimensional data sets. The approach in TDA is twofold; i.e. Persistent homology (PH) which quantifies topological invariants of a given data set, and Mapper which represents the high-dimensional data set into a 1D graph with nodes and edges. In this work, we employ PH as a tool to quantify the first dimensional holes (H1) in the daily rainfall data set collected between 2012 to 2017 from six rainfall stations located in Terengganu, Malaysia. We divided the rainfall data based on one year (365 days) resulting in each station having five sets of rainfall point clouds. Since a rainfall point cloud consists of 1D data set, direct comparison of rainfalls between stations may not show a clear pattern. Thus, we first embed them into point clouds of 10D with time delay τ = 13, using Takens embedding, preserving its original dynamical state. Next, we employ PH to generate persistence diagram to quantify 1D holes (H1) in the rainfall point clouds and record its maximum persistence (H1 lifespan), as its topological feature to characterize the distribution and intensity of rainfall. The first result is; based on past flood events, flood occurred when the year’s average persistence score exceeds 13. The second part of this work involves clustering the stations using two approaches; the standard dynamic time warping (DTW) method which matches the rainfall frequency before computing its dissimilarity distance; and the PH approach using five years maximum H1 lifespan as its distance matrix. The dendrograms produced by both clustering approaches are different, in which DTW has three distinct clusters, but dissimilar to its rainfall distribution. However, PH neatly ranks based on its annual rainfall intensity and recurrence, hence outperforming DTW approach.

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