Assessing dynamic-time- warping dissimilarity measures in regionalization of river discharges

Abstract

Regionalization of river discharges is a process of transferring hydrological information to generalize hydrological information from one river to another. One approach to regionalize river discharge is to use a distance-based regional analysis by employing a Dynamic Time Warping (DTW) dissimilarity measure to cluster homogeneous river discharge patterns based on sequenced of time series discharge data. However, clustering homogeneous river discharge patterns can be sensitive to the choice of distance metric measures used due to out of phase behavior in the discharge time series. In this study, we assess three types of Dynamic Time Warping (DTW) measures specifically conventional DTW, a feature based DTW and a weighted based DTW on four annual discharge time series from six rivers in the state of Johor, Malaysia. A comparison of eight different clustering validation indices to determine the optimal number of rivers clusters with similar discharge patterns. These indices are used to measure the internal and external strength of the identified clusters. The results indicate that weighted based DTW outperform the conventional DTW and feature based DTW with 75% of the clustering indices agree that there are three optimal clusters of river discharge. By using weight as a function in DTW, it helps to cater the out of phase behavior in river discharge time series with the highest agreement of clustering indices compared to other types of DTW measures. We also found that three of the rivers (Sayong, Bekok, and Segamat) have similar river discharge patterns and could be used together in the generalization process. Meanwhile, the other rivers (Johor, Kahang, and Muar) varies in their time series patterns.