Performance of Time-based and Non-time-based Clustering in the Identification of River Discharge Patterns

Abstract

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Citation

Mansor, N.S., Ahmad, N. and Heryansyah, A. (2018), "Performance of Time-based and Non-time-based Clustering in the Identification of River Discharge Patterns", Improving Flood Management, Prediction and Monitoring (Community, Environment and Disaster Risk Management, Vol. 20), Emerald Publishing Limited, Leeds, pp. 133-140. https://doi.org/10.1108/S2040-726220180000020022