Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis

Non-availability of adequate extreme rainfall information at any place of interest are solved using regionalization where subjective grouping of similar attributes of nearby gauged stations is performed. K-Means and Fuzzy C-Means are commonly used methods in regionalization of rainfall, but applicat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nilotpal Debbarma, Parthasarathi Choudhury, Parthajit Roy
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	clustering fuzzy c-means genetic algorithm information transfer index sensitivity Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/af804a94df7f4a3bb410dcc63fe7dd40
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:af804a94df7f4a3bb410dcc63fe7dd40
record_format	dspace
spelling	oai:doaj.org-article:af804a94df7f4a3bb410dcc63fe7dd402021-11-05T21:17:28ZComparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis1751-231X10.2166/wpt.2021.086https://doaj.org/article/af804a94df7f4a3bb410dcc63fe7dd402021-10-01T00:00:00Zhttp://wpt.iwaponline.com/content/16/4/1446https://doaj.org/toc/1751-231XNon-availability of adequate extreme rainfall information at any place of interest are solved using regionalization where subjective grouping of similar attributes of nearby gauged stations is performed. K-Means and Fuzzy C-Means are commonly used methods in regionalization of rainfall, but application of genetic algorithms is very rarely explored. Genetic algorithms (GA) are highly efficient evolutionary algorithms, and through an appropriate objective function can effectively achieve the purpose of clustering. In the present study, Davies-Bouldin index is considered and validation is performed using a set of validation measures. Taking into account the varied output obtained in each validation measure, an ensemble approach involving multi criteria decision making is applied to obtain optimal ranked solutions, and the procedure is extended to K-Means and Fuzzy C-Means for comparison. From the results obtained, GA-based clustering is found to outperform the other two algorithms in formation of homogenous regions with better performance in leave-one-out cross validation (LOOCV) test and sensitivity analysis. Accuracy of regional growth curves of regions assessed using regional relative bias and RMSE suggest low uncertainty and accurate quantile estimates in GA regions. Further, information transfer index based on entropy evaluated among GA regions is found to be highest and K-Means lowest. HIGHLIGHTS Comparison of genetic algorithm-based clustering to K-Means and Fuzzy C-Means with ensemble MCDM technique.; Optimum cluster based on several cluster validation indices and MCDM.; Sensitivity analysis of MCDM rankings.; Regional growth curve comparision for regions delineated by all methods.; Information transfer among stations in cluster regions with entropy based information transfer index.;Nilotpal DebbarmaParthasarathi ChoudhuryParthajit RoyIWA Publishingarticleclusteringfuzzy c-meansgenetic algorithminformation transfer indexsensitivityEnvironmental technology. Sanitary engineeringTD1-1066ENWater Practice and Technology, Vol 16, Iss 4, Pp 1446-1464 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clustering fuzzy c-means genetic algorithm information transfer index sensitivity Environmental technology. Sanitary engineering TD1-1066
spellingShingle	clustering fuzzy c-means genetic algorithm information transfer index sensitivity Environmental technology. Sanitary engineering TD1-1066 Nilotpal Debbarma Parthasarathi Choudhury Parthajit Roy Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
description	Non-availability of adequate extreme rainfall information at any place of interest are solved using regionalization where subjective grouping of similar attributes of nearby gauged stations is performed. K-Means and Fuzzy C-Means are commonly used methods in regionalization of rainfall, but application of genetic algorithms is very rarely explored. Genetic algorithms (GA) are highly efficient evolutionary algorithms, and through an appropriate objective function can effectively achieve the purpose of clustering. In the present study, Davies-Bouldin index is considered and validation is performed using a set of validation measures. Taking into account the varied output obtained in each validation measure, an ensemble approach involving multi criteria decision making is applied to obtain optimal ranked solutions, and the procedure is extended to K-Means and Fuzzy C-Means for comparison. From the results obtained, GA-based clustering is found to outperform the other two algorithms in formation of homogenous regions with better performance in leave-one-out cross validation (LOOCV) test and sensitivity analysis. Accuracy of regional growth curves of regions assessed using regional relative bias and RMSE suggest low uncertainty and accurate quantile estimates in GA regions. Further, information transfer index based on entropy evaluated among GA regions is found to be highest and K-Means lowest. HIGHLIGHTS Comparison of genetic algorithm-based clustering to K-Means and Fuzzy C-Means with ensemble MCDM technique.; Optimum cluster based on several cluster validation indices and MCDM.; Sensitivity analysis of MCDM rankings.; Regional growth curve comparision for regions delineated by all methods.; Information transfer among stations in cluster regions with entropy based information transfer index.;
format	article
author	Nilotpal Debbarma Parthasarathi Choudhury Parthajit Roy
author_facet	Nilotpal Debbarma Parthasarathi Choudhury Parthajit Roy
author_sort	Nilotpal Debbarma
title	Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
title_short	Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
title_full	Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
title_fullStr	Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
title_full_unstemmed	Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
title_sort	comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/af804a94df7f4a3bb410dcc63fe7dd40
work_keys_str_mv	AT nilotpaldebbarma comparisionofperformanceofmulticriteriadecisionmakingensembleclusteringalgorithmsinrainfallfrequencyanalysis AT parthasarathichoudhury comparisionofperformanceofmulticriteriadecisionmakingensembleclusteringalgorithmsinrainfallfrequencyanalysis AT parthajitroy comparisionofperformanceofmulticriteriadecisionmakingensembleclusteringalgorithmsinrainfallfrequencyanalysis
_version_	1718444001548304384

Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis

Ejemplares similares