Discovering flood rising pattern in hydrological time series data mining during the pre monsoon period

Abstract

<span style="font-size:11.0pt;mso-bidi-font-size:10.0pt;line-height:115%;font-family: Calibri;mso-fareast-font-family:" times="" new="" roman";mso-bidi-font-family:mangal;="" mso-ansi-language:en-in;mso-fareast-language:en-in;mso-bidi-language:hi;="" mso-bidi-font-style:italic"="">Present study examines the flood rising pattern for the river discharge data in the river Brahmaputra basin. The months from January to May comes under the pre monsoon season. In this paper, with the help of time series data mining techniques, analysis has made for <span style="font-size:11.0pt;mso-bidi-font-size:10.0pt; line-height:115%;font-family:Calibri;mso-fareast-font-family:" times="" new="" roman";="" mso-bidi-font-family:mangal;mso-ansi-language:en-in;mso-fareast-language:en-in;="" mso-bidi-language:hi;mso-bidi-font-weight:bold"="">hydrological daily discharge time series data, measured at the Panchratna station during the pre monsoon in the river Brahmaputra under Brahmaputra and Barak Basin Organization before coming the high flood.<span style="font-size:11.0pt;mso-bidi-font-size:10.0pt;line-height:115%;font-family: Calibri;mso-fareast-font-family:" times="" new="" roman";mso-bidi-font-family:mangal;="" mso-ansi-language:en-in;mso-fareast-language:en-in;mso-bidi-language:hi"=""> <span style="font-size:11.0pt;mso-bidi-font-size:10.0pt;line-height:115%;font-family: Calibri;mso-fareast-font-family:" times="" new="" roman";mso-bidi-font-family:arial;="" mso-ansi-language:en-in;mso-fareast-language:en-in;mso-bidi-language:hi;="" mso-bidi-font-weight:bold"="">Statistical analysis has made for standardization of data. K-means clustering, Dynamic Time Warping (DTW), A<span style="font-size:11.0pt;mso-bidi-font-size:10.0pt;line-height:115%;font-family: Calibri;mso-fareast-font-family:" times="" new="" roman";mso-bidi-font-family:mangal;="" mso-ansi-language:en-in;mso-fareast-language:en-in;mso-bidi-language:hi;="" mso-bidi-font-weight:bold"="">gglomerative Hierarchical Clustering (AHC), Ward’s criterion and regression analysis 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 flood rising pattern, Gauge–Discharge Curve, Water Level Hydrographs, Rainfall Bar Graphs, Mean maximum -minimum temperature and evaporation graphs have been developed and also discharge rising coefficient has been calculated. This study gives the behavioral characteristics of rivers discharge during rising of high floods with the time series data mining. </span></span></span></span></span>