Modularity Density Based-Edge Betweenness Method for Catchment Classification in Sabah

Authors

  • Siti Aisyah Tumiran Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS 88400 Kota Kinabalu, Sabah, Malaysia
  • Farah Nurul Ain Mohd Suffian@Laurance Mathematics Visualization (MathViz) Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
  • Suzelawati Zenian Mathematics Visualization (MathViz) Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
  • Kawi Bin Bidin Mathematics Visualization (MathViz) Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.

DOI:

https://doi.org/10.37934/araset.45.2.6777

Keywords:

Community Structure, Modularity, Edge Betwenness, Streamflow

Abstract

One of the most significant studies in hydrology is the classification of catchments. There are various reasons why catchment classification studies have been conducted, but most importantly is to anticipate the prediction of ungauged basin (PUB), among others. There are several ways of classification, each with its own set of assumptions and basis, that have been used for catchment classification. Recently, complex network concepts, specifically community structure, have emerged as key classification methods, and are now attaining traction in catchment classification. As a result, in this study, community structure approach, specifically the Modularity Density based- Edge Betweenness (MDEB) method is implemented to split the network into communities. By using the proposed method, a network of 30 monthly streamflow stations across Sabah is considered for catchment classification. The impact of correlation threshold, which vary from 0 to 1, denoting the strength between a pair of catchments is also investigated. As a result, four threshold values are chosen (T = 0.5, 0.55, 0.6, and 0.65), and the communities established with threshold value, T=0.6 are interpreted. The relationship between catchment characteristics and flow characteristics are investigated as well as distance-correlation relationships for communities identified to understand the Sabah catchment’s behaviors.

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Author Biographies

Siti Aisyah Tumiran, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS 88400 Kota Kinabalu, Sabah, Malaysia

sitiaisyah.tumiran@ums.edu.my

Farah Nurul Ain Mohd Suffian@Laurance, Mathematics Visualization (MathViz) Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.

bs19110258@student.ums.edu.my

Suzelawati Zenian, Mathematics Visualization (MathViz) Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.

suzela@ums.edu.my

Kawi Bin Bidin, Mathematics Visualization (MathViz) Research Group, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.

kbidin@ums.edu.my

Published

2024-04-14

Issue

Section

Articles