Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 67 - Number 6
Year of Publication: 2013
Satanand Mishra
V. K. Dwivedi
C. Sarvanan
K. K. Pathak

Satanand Mishra, V K Dwivedi, C Sarvanan and K K Pathak. Article: Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin. International Journal of Computer Applications 67(6):7-14, April 2013. Full text available. BibTeX

	author = {Satanand Mishra and V. K. Dwivedi and C. Sarvanan and K. K. Pathak},
	title = {Article: Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {67},
	number = {6},
	pages = {7-14},
	month = {April},
	note = {Full text available}


In this paper, based on data mining techniques, the analysis is carried out in hydrological daily discharge time series of the Panchratna station in the river Brahmaputra under Brahmaputra and Barak Basin Organization in India. The data has selected for the high flood years 1988, 1991,1998, 2004, and 2007. The whole year is divided into three periods known as Pre-monsoon, Monsoon and Post Monsoon. In this paper, only monsoon period data have been used. For standardization of data, statistical analysis such as mean monthly discharge, monthly Maximum Discharge, monthly amplitude and monthly standard deviation have been carried out. K-means clustering is segmented for the monsoon period process of daily discharge. Dynamic Time Warping (DTW) is used to look for similarities in the discharge process under the same climatic condition. Similarity matrix helped in the mining of discharge process in similar time period in the different years. The agglomerative hierarchical clustering is used to cluster and discover the discharge patterns in terms of the autoregressive model. A forecast model has been predicted on the discharge process.


  • Anuradha, K. , and Sairam, N. 2011, Classification of Images using JACCARD co-efficient and higher –order co-occurrences, JATTI, Vol. -34 No. 1, 100-105.
  • Aiyun, L. , and Jiahai, L. 2011, Forecasting monthly runoff using weblet neural network model. International conference on Mechatronic Science, Electronic Engineering and Computer. IEEE. 978-1-61284-722-1 .
  • Aydin, I. , Karakose, and Akin, A. , 2009, The prediction Algorithm based on Fuzzy logic using time series data mining method, World Academy of Science, Engg. and Technology, 51.
  • Feng, L. H. , Zhang, J. Z. , 2010, Application of ANN in Forecast of surface runoff, IEEE (INC), 2010.
  • Giorgino, T. 2009, Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package, Journal of Statistical Software, 31, Issue 7.
  • Han, J. W. , and Kamber, M. 2001, Data Mining Concept and techniques, Morgan Kaufman: San Fransciko, CA.
  • Jingwen, XU. 2009, Mid-short term daily runoff forecasting by ANNs and multiple process based hydrological models, IEE, Conference : YC-ICT, 526-529.
  • Kadir, A. , and Peker, 2005, Subsequence Time Series (STS) Clustering Techniques for Meaningful Pattern Discovery, KIMAS 2005, April 18-21, Waltham, MA,USA, IEEE,0-7803-9013.
  • Kanungo,T. , Mount, D. M. , Netanyahu, N. S. , Piatko, C. D. , Silverman, R. , and Wu, A. Y. 2002, an efficient k-Means clustering algorithm: analysis and implementation, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 24, NO. 7, 881-892.
  • Li, C. , and Yuan, X. 2008, Research and Application of Data Mining for Runoff Forecasting, IEEE, 978-0-7695-3357-5/08.
  • Liang, X. , and Liang, Y. 2001, Applications of Data Mining in Hydrology, ICDM0, 1-0-7695-1119-8/01 ©2001 IEEE.
  • Liao, T. W. 2005, Clustering of time series data—a survey, Pattern Recognition, 38, 2005, 1857–1874.
  • Ni, X. 2008, Research of Data mining based on neural Networks, WASET, 39, 2008.
  • Ouyang, R. , Ren, L. , Cheng, W. , and Zhou, C. , 2010, "Similarity search and pattern discovery in Hydrological time series data mining, Wiley InterScience , Hydrol. Process,24, 1198-1210.
  • Piatetsky-Shapiro, G. , Frawley, W. J. 1991. Knowledge Discovery in Databases, AAAI/MIT Press: Boston, MA.
  • Rafiq, M. I. , Martin, J. , Connor, O. , and Das, A. K. 2005, Computational method for Temporal Pattern Discovery in Biomedical Genomic Database, IEEE(CSB'05), 0-7695-2344-7/05.
  • Ruhana, K. , Mohamud, K. , Zakaria, N. , Katuk, N. , and Shbier M. 2009, Flood Pattern Detection Using Sliding Window Techniques, 978-0-7695-3648-4/09, IEE DOI 10. 1109/AMS,2009, 15.
  • Samsudin, R. , Saad, P. , and Shabri , A. 2011, River flow time series using least square support vector machines, Hydrol,Earth Syst. Sci, 15, 1835-1852, 2011.
  • Sihui, D. 2009, Forecast Model of Hydrologic Single, Element Medium and Long-Period Based on Rough Set Theory, IEEE, 978—07695-3735-1/09.
  • Spate, J. M. , Croke, B. F. W. , and Jakeman, A. J. , 2003, Data Mining in Hydrology, Conference: MODSIM , 2003.
  • Tapas, K. , David, M. , Nathan, S. , Christine, D. , and Angela, Y. 2002, An efficient k-Means Clustering Algorithm: Analysis and Implementation", 0162-8828/02,2002 IEE 24, No. 7.
  • Weilin, L. 2011, Neural network model for hydrological forecasting based on multivariate phase space reconstruction, IEEE, Vol. ,2,663-667.
  • Wiriyarattanakul, S. , Auephanwiriyakull, S. , Theera, and Umpon, N, 2008, Runoff Forecasting using Fuzzy Support Vector Regression, IEEE, 978-1-4244-2565-5/08.
  • Wu, C. L. , and Chaw, K. W. , 2010, Data-driven models for monthly stream flow time series Prediction, Engineering Applications of Artificial Intelligence, Vol. 23, Issue 8, 2010, 1350-1367.
  • Yuelong, Z. , Shijin, L. , Dingsheng, W. , and Xiaohua ,Z. 2008, A Novel Approach to the Similarity Analysis of Multivibrate Time series and its Application in Hydrological Data mining, 978-07695-3336-0/08, IEE DOI 10. 1109/CSSE. 2008. 1064.