Abstract
In the wake of global water scarcity, forecasting of water quantity and quality, regionalization of river basins has attracted serious attention of the hydrology researchers. It has become an important area of research to enhance the quality of prediction of yield in river basins. In this paper, we analyzed the data of Godavari basin, and regionalize it using a cluster ensemble method. Cluster Ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous basins. The goal is to identify, analyse and describe hydrologically similar catchments using cluster analysis. Clustering has been done using RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. Clustering results of RCDA algorithm have been compared with Best of k-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the Godavari basin data.
Highlights
Estimating design flow of ungauged basins is very crucial in the planning and management of hydraulic and water resources engineering
Computational validation of results is performed by comparing the Sum of Squared error (SSE) (Sum of Squared Error) of the clustering scheme obtained by Robust Clustering Using Discriminant Analysis (RCDA), with another cluster ensemble method available in R software and the best of the constituent clustering scheme
We get the optimum partition H = 8, because at this value of partition, we obtained the lowest value of SSE (Sum of Squared Error) and maximum clustering quality
Summary
Estimating design flow of ungauged basins is very crucial in the planning and management of hydraulic and water resources engineering. Regionalization is defined as determination of hydrologically similar units, and is one of the most challenging tasks in surface hydrology. Regionalization is done for estimating design flow in ungauged basins which is frequently encountered in the design and planning of hydraulic and water resources engineering [2]. Runoff predictions in ungauged catchments are determined by regionalization. Development of practical runoff prediction methods are important for assessing water resources in an ungauged or poorly gauged catchment which is usually located in headwater regions [2]. Catchment shows a wide range of response behaviour, Regionalization is utilized for searching the hydrological similarity of catchments to characterize each catchment [3]
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