Discovering Flood Recession Pattern in Hydrological Time Series Data Mining during the Post Monsoon Period

Abstract

his paper examines the flood recession pattern for the river discharge data in the river Brahmaputra basin. The months from October to December comes under the post monsoon season. In this paper, with the help of time series data mining techniques, the analysis has made for hydrological daily discharge time series data, measured at the Panchratna station during the post monsoon in the river Brahmaputra under Brahmaputra and Barak Basin Organization after the high flood. Statistical analysis has made for standardization of data. K-means clustering, Dynamic Time Warping(DTW), Agglomerative Hierarchical Clustering(AHC) and Ward's criterion are used to cluster and discover the discharge patterns in terms of the autoregressive model. A forecast model has been developed for the discharge process. For validation of the recession pattern, Gauge-Discharge Curve, Water Label Hydrographs, Rainfall Bar Graphs have been developed and also discharge recession coefficient has been calculated. This study gives the behavioral characteristics of rivers discharge during recession of high floods with the time series data mining.