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Sains Malaysiana 51(7)(2022):
http://doi.org/10.17576/jsm-2022-5107-22
Streamflow Data Analysis for Flood
Detection using Persistent Homology
(Analisis Data
Aliran Sungai bagi Pengesanan Banjir menggunakan Homologi Gigih)
SYED
MOHAMAD SADIQ SYED MUSA*, MOHD SALMI MD NOORANI, FATIMAH ABDUL
RAZAK, MUNIRA ISMAIL & MOHD ALMIE ALIAS
Department of
Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi,
Selangor Darul Ehsan, Malaysia
Diserahkan: 20
Mei 2021/Diterima: 1 Disember 2021
Abstract
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.
Keywords:
Flood; persistent homology; streamflow; time delay embedding; topological data
analysis
Abstrak
Banjir merupakan bencana alam yang berlaku hampir di seluruh dunia. Analisis data aliran sungai mampu memberikan maklumat iklim yang penting bagi kejadian banjir. Homologi gigih (HG), suatu alat analisis baharu dalam bidang analisis data bertopologi (ADB) menawarkan pendekatan baharu bagi mendapatkan maklumat dalam suatu set data menggunakan pendekatan kualitatif. HG menggunakan konsep topologi untuk mendapatkan maklumat berkaitan ciri topologi seperti komponen berkait, lubang dan lompong yang hadir dalam set data tersebut. Kajian ini membentangkan pendekatan baharu bagi analisis data aliran sungai bagi pengesanan banjir menggunakan kaedah HG. Suatu analisis telah dijalankan di Sungai Kelantan, Malaysia. Hasil kajian menunjukkan bahawa HG memberikan corak ciri-ciri topologi data aliran sungai yang berbeza bagi musim kering dan banjir. Secara khususnya, terdapat lebih banyak ciri topologi yang gigih dalam bentuk komponen berkait and lubang pada
data musim banjir berbanding musim kering. Hasil kajian juga menunjukkan bahawa data siri masa ukuran jarak berkaitan perubahan komponen berkait adalah konsisten dengan data siri masa aliran sungai. Kesimpulannya, kajian ini mencadangkan data siri masa ukuran jarak berkaitan perubahan komponen berkait boleh digunakan sebagai ukuran bagi pengesanan banjir di Sungai Kelantan, Malaysia.
Kata kunci: Analisis data bertopologi; arus sungai; banjir; homologi gigih; pembenaman masa penangguhan
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*Pengarang untuk surat-menyurat; email:
syedmohdsadiq1992@yahoo.com
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