A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions
Flood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity an...
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MDPI AG
2023-06-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/6/126 |
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| author | Ajla Mulaomerović-Šeta Borislava Blagojević Vladislava Mihailović Andrea Petroselli |
| author_facet | Ajla Mulaomerović-Šeta Borislava Blagojević Vladislava Mihailović Andrea Petroselli |
| author_sort | Ajla Mulaomerović-Šeta |
| collection | DOAJ |
| description | Flood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity and widespread use, a combination of hierarchical clustering by Ward’s algorithm and the index-flood method is applied in this research. While hierarchical clustering is very efficient, its shortcomings are the lack of flexibility in the definition of clusters/regions and the inability to transfer objects/stations from one cluster center to another. To overcome this, using silhouette width for induced clustering of stations in flood studies is proposed in this paper. A regionalization procedure is conducted on 53 gauging stations under a continental climate in the West Balkans. In the induced clustering, a negative silhouette width is used as an indicator for the relocation of station(s) to another cluster. The estimates of mean annual flood and 100-year flood quantiles assessed by the original and induced clustering are compared. A jackknife procedure is applied for mean annual flood estimation and 100-year flood quantiles. Both the Hosking–Wallis and Anderson–Darling bootstrap tests provide better results regarding the homogeneity of the defined regions for the induced clustering compared to the original one. The goodness-of-fit measures indicate improved clustering results by the proposed intervention, reflecting flood quantile estimation at the stations with significant overestimation by the original clustering. |
| first_indexed | 2024-03-11T02:23:28Z |
| format | Article |
| id | doaj.art-6a4e030c785e4e2b87438f4164d39ad1 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-11T02:23:28Z |
| publishDate | 2023-06-01 |
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| series | Hydrology |
| spelling | doaj.art-6a4e030c785e4e2b87438f4164d39ad12023-11-18T10:42:36ZengMDPI AGHydrology2306-53382023-06-0110612610.3390/hydrology10060126A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation RegionsAjla Mulaomerović-Šeta0Borislava Blagojević1Vladislava Mihailović2Andrea Petroselli3Faculty of Civil Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and HerzegovinaFaculty of Civil Engineering and Architecture, University of Niš, 18000 Niš, SerbiaFaculty of Forestry, University of Belgrade, 11000 Belgrade, SerbiaDepartment of Economics, Engineering, Society and Business Organization (DEIM), Tuscia University, 01100 Viterbo, ItalyFlood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity and widespread use, a combination of hierarchical clustering by Ward’s algorithm and the index-flood method is applied in this research. While hierarchical clustering is very efficient, its shortcomings are the lack of flexibility in the definition of clusters/regions and the inability to transfer objects/stations from one cluster center to another. To overcome this, using silhouette width for induced clustering of stations in flood studies is proposed in this paper. A regionalization procedure is conducted on 53 gauging stations under a continental climate in the West Balkans. In the induced clustering, a negative silhouette width is used as an indicator for the relocation of station(s) to another cluster. The estimates of mean annual flood and 100-year flood quantiles assessed by the original and induced clustering are compared. A jackknife procedure is applied for mean annual flood estimation and 100-year flood quantiles. Both the Hosking–Wallis and Anderson–Darling bootstrap tests provide better results regarding the homogeneity of the defined regions for the induced clustering compared to the original one. The goodness-of-fit measures indicate improved clustering results by the proposed intervention, reflecting flood quantile estimation at the stations with significant overestimation by the original clustering.https://www.mdpi.com/2306-5338/10/6/126regional analysisflood quantilehierarchical clusteringsilhouette width |
| spellingShingle | Ajla Mulaomerović-Šeta Borislava Blagojević Vladislava Mihailović Andrea Petroselli A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions Hydrology regional analysis flood quantile hierarchical clustering silhouette width |
| title | A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions |
| title_full | A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions |
| title_fullStr | A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions |
| title_full_unstemmed | A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions |
| title_short | A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions |
| title_sort | silhouette width induced hierarchical clustering for defining flood estimation regions |
| topic | regional analysis flood quantile hierarchical clustering silhouette width |
| url | https://www.mdpi.com/2306-5338/10/6/126 |
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