Journal of Software Engineering and Applications

Volume 5, Issue 8 (August 2012)

ISSN Print: 1945-3116   ISSN Online: 1945-3124

Google-based Impact Factor: 1.22  Citations  h5-index & Ranking

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

HTML  Download Download as PDF (Size: 1164KB)  PP. 568-573  
DOI: 10.4236/jsea.2012.58065    4,701 Downloads   7,352 Views  Citations

ABSTRACT

The magnitude and frequency of precipitation is of great significance in the field of hydrologic and hydraulic design and has wide applications in varied areas. However, the availability of precipitation data is limited to a few areas, where the rain gauges are successfully and efficiently installed. The magnitude and frequency of precipitation in ungauged sites can be assessed by grouping areas with similar characteristics. The procedure of grouping of areas having similar behaviour is termed as Regionalization. In this paper, RCDA cluster ensemble algorithm is employed to identify the homogeneous regions of rainfall in India. 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 regions. The goal is to identify, analyse and describe hydrologically similar regions using RCDA cluster ensemble algorithm. 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. RCDA algorithm has 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 rainfall in different parts of India.

Share and Cite:

S. Ahuja and C. Dhanya, "Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India," Journal of Software Engineering and Applications, Vol. 5 No. 8, 2012, pp. 568-573. doi: 10.4236/jsea.2012.58065.

Cited by

[1] Climate regionalization to assess change in extreme rainfall over Indian subcontinent
2022 3rd URSI Atlantic and Asia Pacific …, 2022
[2] Regionalización de precipitación máxima diaria en Moquegua Perú
Quispe, E Flores-Condori… - Revista Científica de …, 2022
[3] Regional Frequency Analysis Using L-Moment Methodology—A Review
2021
[4] Some non-uniformity patterns spread over the lower Mahanadi River Basin, India
Geocarto International, 2021
[5] Recent changes in Indian monsoon in light of regionalization based on various rain features
Theoretical and Applied Climatology, 2021
[6] REGIONALIZATION OF RAINFALL IN NORTHEASTERN THAILAND
2020
[7] Homogeneous regionalization via L-moments for Mumbai City, India
2019
[8] A Machine Learning Approach to Re-Classification of Climate Zones Based on Multiple Rain Features Over India
2019
[9] Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India
2018
[10] Regionalization of rainfall characteristics in India incorporating climatic variables and using self-organizing maps
Journal of Electromagnetic Waves and Applications, 2017
[11] A Novel Ensemble Clustering for Operational Transients Classification with Application to a Nuclear Power Plant Turbine
2015
[12] K-Means clustering technique applied to availability of micro hydro power
Sustainable Energy Technologies and Assessments, 2014
[13] Regionalization of Precipitation in India–A Review
Journal of the Indian Institute of Science, 2013
[14] A MULTIVARIATE APPROACH FOR STUDYING THE RAINFALL PATTERN IN VISAKHAPATNAM DISTRICT
Thesis, 2013

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.