ASSESSING DYNAMIC-TIME-WARPING DISSIMILARITY MEASURES IN REGIONALIZATION OF RIVER DISCHARGES
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Published:
Sep 30, 2019
Keywords:
Dynamic Time Warping Clustering Dissimilarity measure
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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.
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How to Cite
Mansor, N. S., Ahmad, N., & Heryansyah, A. (2019). ASSESSING DYNAMIC-TIME-WARPING DISSIMILARITY MEASURES IN REGIONALIZATION OF RIVER DISCHARGES. Malaysian Journal of Science, 38(Sp2), 14–22. https://doi.org/10.22452/mjs.sp2019no2.2
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ISMI-ICTAS18 (Published)
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Jeong, Y. S., Jeong, M. K., and Omitaomu, O. A. (2011). Weighted dynamic time warping for time series classification. Pattern Recognition, 44(9), 2231-2240.
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Mishra, S.; Saravanan, C.; Dwivedi, V. and Pathak, K. (2015). Discovering flood rising pattern in hydrological time series data mining during the pre monsoon period. Indian Journal of Geo-Marine Sciences, 44: 3.
Ouyang, R.; Ren, L.; Cheng, W. and Zhou, C. (2010). Similarity search and pattern discovery in hydrological time series data mining. Hydrological Processes, 24(9): 1198-1210.
Razavi, T. & Coulibaly, P. (2012). Streamflow prediction in ungauged basins: review of regionalization methods. Journal of Hydrologic Engineering, American Society of Civil Engineers,18: 958-975.
Snelder, T. H.; Biggs, B. J. & Woods, R. A. (2005). Improved eco-hydrological classification of rivers. River Research and Applications, 21(6): 609-628.
Toth, E. (2013). Catchment classification based on characterisation of streamflow and precipitation time series. Hydrology and Earth System Sciences, 17: 1149-1159.
Zhao, Q. (2012). Cluster Validity in Clustering Methods. Dissertations in Forestry and Natural Sciences, University of Eastern Finland.
Corduas, M. (2011). Clustering streamflow time series for regional classification. Journal of Hydrology, 407: 73-80.
Desgraupes, B. (2013). Clustering indices. University of Paris Ouest-Lab Modal’X, 1: 34.
Dikbas, F.; Firat, M.; Koc, A. C. and Gungor, M. (2013). Defining homogeneous regions for streamflow processes in Turkey using a K-means clustering method. Arabian Journal for Science and Engineering, 38: 1313-1319.
Elesbon, A. A.; Silva, D. D. d.; Sediyama, G. C.; Guedes, H. A.; Ribeiro, C. A. and Ribeiro, C. B. d. M. (2015). Multivariate statistical analysis to support the minimum streamflow regionalization. Engenharia Agricola, SciELO Brasil, 35: 838-851.
Geertsema, T.; Teuling, A.; Uijlenhoet, R.; Torfs, P.; Hoitink, A. and Weerts, A. (2016). Simultaneous occurrence of discharge peaks in a large river and its lowland tributaries. In River Flow - Proceedings of the International Conference on Fluvial Hydraulics, St. Louis, 11-14 July 2017, pp. 1626-1630.
Gupta, A. and Chaturvedi, S. K. (2013). Real Time Prediction System of Discharge of the Rivers using Clustering Technique of Data Mining.
International Journal of Engineering Research and Development, 9: 12-24.
Jeong, Y. S., Jeong, M. K., and Omitaomu, O. A. (2011). Weighted dynamic time warping for time series classification. Pattern Recognition, 44(9), 2231-2240.
Kahya, E.; Demirel, M. Cü. and Piechota, T. C. (2007). Spatial grouping of annual streamflow patterns in Turkey. 27th AGU Hydrology Days, 169-176
Laaha, G. and Blöschl, Gü. (2006). A comparison of low flow regionalisation methods: catchment grouping. Journal of Hydrology, Elsevier,323: 193-214.
Mishra, S.; Saravanan, C.; Dwivedi, V. and Pathak, K. (2015). Discovering flood rising pattern in hydrological time series data mining during the pre monsoon period. Indian Journal of Geo-Marine Sciences, 44: 3.
Ouyang, R.; Ren, L.; Cheng, W. and Zhou, C. (2010). Similarity search and pattern discovery in hydrological time series data mining. Hydrological Processes, 24(9): 1198-1210.
Razavi, T. & Coulibaly, P. (2012). Streamflow prediction in ungauged basins: review of regionalization methods. Journal of Hydrologic Engineering, American Society of Civil Engineers,18: 958-975.
Snelder, T. H.; Biggs, B. J. & Woods, R. A. (2005). Improved eco-hydrological classification of rivers. River Research and Applications, 21(6): 609-628.
Toth, E. (2013). Catchment classification based on characterisation of streamflow and precipitation time series. Hydrology and Earth System Sciences, 17: 1149-1159.
Zhao, Q. (2012). Cluster Validity in Clustering Methods. Dissertations in Forestry and Natural Sciences, University of Eastern Finland.