Sains Malaysiana 51(7)(2022): 2211-2222

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

Received: 20 May 2021/Accepted: 1 December 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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^*Corresponding author; email: syedmohdsadiq1992@yahoo.com