A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters
One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved mus...
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doaj-f4a22e20d3a3441b8dfd21154996ff7c2021-09-06T19:40:48ZengSciendoJournal of Hydrology and Hydromechanics0042-790X2017-12-0165435936510.1515/johh-2017-0024johh-2017-0024A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clustersSenent-Aparicio Javier0Soto Jesús1Pérez-Sánchez Julio2Garrido Jorge3Civil Engineering Department, UCAM Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos, nº 135, 30107 Murcia, SpainCivil Engineering Department, UCAM Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos, nº 135, 30107 Murcia, SpainCivil Engineering Department, UCAM Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos, nº 135, 30107 Murcia, SpainCivil Engineering Department, UCAM Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos, nº 135, 30107 Murcia, SpainOne of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.https://doi.org/10.1515/johh-2017-0024fuzzy clusteringregionalisationalto genilhydrological homogeneityregional flood frequency analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Senent-Aparicio Javier Soto Jesús Pérez-Sánchez Julio Garrido Jorge |
spellingShingle |
Senent-Aparicio Javier Soto Jesús Pérez-Sánchez Julio Garrido Jorge A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters Journal of Hydrology and Hydromechanics fuzzy clustering regionalisation alto genil hydrological homogeneity regional flood frequency analysis |
author_facet |
Senent-Aparicio Javier Soto Jesús Pérez-Sánchez Julio Garrido Jorge |
author_sort |
Senent-Aparicio Javier |
title |
A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters |
title_short |
A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters |
title_full |
A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters |
title_fullStr |
A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters |
title_full_unstemmed |
A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters |
title_sort |
novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters |
publisher |
Sciendo |
series |
Journal of Hydrology and Hydromechanics |
issn |
0042-790X |
publishDate |
2017-12-01 |
description |
One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis. |
topic |
fuzzy clustering regionalisation alto genil hydrological homogeneity regional flood frequency analysis |
url |
https://doi.org/10.1515/johh-2017-0024 |
work_keys_str_mv |
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