Keywords
Flood event, classification algorithms, time-delay embedding, variable selection
Start Date
26-6-2018 5:00 PM
End Date
26-6-2018 7:00 PM
Abstract
Application of supervised classification to short-term predictions of hydrological events rely on data routinely collected by Conservation Authorities with high frequencies on streams and their watersheds. It implies that the quality of predictions depends on location of monitoring stations and representativeness of data sets used in the analysis. Given that application of classification algorithms requires data transformation, an attempt was made to improve the performance of these algorithms by extending the set of variables of black box models which are supplied by stream and rain gauges and include their derivatives which may carry very important information as well. The original computational scheme was based on time-delay embedding applied to data from all observation sites of a watershed. The variable selection was implemented using both hydrological knowledge and computational procedures. The computational experiments were conducted on data of various granularity and years with different hydrological characteristics. The results of the study are presented in the paper.
Variable selection for improving predictions of hydrological events
Application of supervised classification to short-term predictions of hydrological events rely on data routinely collected by Conservation Authorities with high frequencies on streams and their watersheds. It implies that the quality of predictions depends on location of monitoring stations and representativeness of data sets used in the analysis. Given that application of classification algorithms requires data transformation, an attempt was made to improve the performance of these algorithms by extending the set of variables of black box models which are supplied by stream and rain gauges and include their derivatives which may carry very important information as well. The original computational scheme was based on time-delay embedding applied to data from all observation sites of a watershed. The variable selection was implemented using both hydrological knowledge and computational procedures. The computational experiments were conducted on data of various granularity and years with different hydrological characteristics. The results of the study are presented in the paper.
Stream and Session
Stream B: (Big) Data Solutions for Planning, Management, and Operation and Environmental Systems
Session B2: Hybrid modelling and innovative data analysis for integrated environmental decision support