Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms
ABSTRACT Three clustering algorithms, K‐means clustering analysis (KCA), fuzzy cluster analysis (FCA), and density‐based spatial clustering of applications with noise (DBSCAN), are applied to classify the 13 subbasins of the Mahe River, southwest India, based on 13 morphometric parameters of each. S...
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Wiley
2025-06-01
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| Series: | Journal of Flood Risk Management |
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| Online Access: | https://doi.org/10.1111/jfr3.70079 |
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| author | Ajith G. Nair R. Kiran |
| author_facet | Ajith G. Nair R. Kiran |
| author_sort | Ajith G. Nair |
| collection | DOAJ |
| description | ABSTRACT Three clustering algorithms, K‐means clustering analysis (KCA), fuzzy cluster analysis (FCA), and density‐based spatial clustering of applications with noise (DBSCAN), are applied to classify the 13 subbasins of the Mahe River, southwest India, based on 13 morphometric parameters of each. Suitable validation indices, such as Davies–Bouldin and Calinski–Harabasz indices, have been used to select the optimal number of clusters using KCA and FCA techniques. All three analyses have yielded three clusters, with subbasins 3–8 forming the first one. These constitute 23% of the total basin area of the Mahe. SW 12 forms a grouping of its own. The rest, SW 1–2, 9–11, and 13, form the third cluster. The first cluster corresponds to the subbasins identified as most susceptible to flooding. Cluster 3 encompasses the subbasins falling in the “Moderate” and “Least” categories with respect to the risk of flooding. The subbasin 12 (< 1 km2) exhibits a deviant morphometric pattern likely due to its specific topographical and network characteristics. The study reveals that cluster algorithms are effective in ranking and prioritizing subbasins of a river based on their potential for natural hazards like flooding. Moreover, the DBSCAN averts the use of cluster validation indices to determine the optimum clusters without compromising the results. All these methods would be beneficial in chalking out suitable management measures for different subbasins of a river based on their potential for any given hazard. |
| format | Article |
| id | doaj-art-53a0fd9dbba54d3c9d55fedac320b770 |
| institution | Kabale University |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-53a0fd9dbba54d3c9d55fedac320b7702025-08-20T03:28:01ZengWileyJournal of Flood Risk Management1753-318X2025-06-01182n/an/a10.1111/jfr3.70079Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster AlgorithmsAjith G. Nair0R. Kiran1Department of Civil Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala IndiaDepartment of Electronics and Communication Engineering Government Engineering College Idukki Idukki Kerala IndiaABSTRACT Three clustering algorithms, K‐means clustering analysis (KCA), fuzzy cluster analysis (FCA), and density‐based spatial clustering of applications with noise (DBSCAN), are applied to classify the 13 subbasins of the Mahe River, southwest India, based on 13 morphometric parameters of each. Suitable validation indices, such as Davies–Bouldin and Calinski–Harabasz indices, have been used to select the optimal number of clusters using KCA and FCA techniques. All three analyses have yielded three clusters, with subbasins 3–8 forming the first one. These constitute 23% of the total basin area of the Mahe. SW 12 forms a grouping of its own. The rest, SW 1–2, 9–11, and 13, form the third cluster. The first cluster corresponds to the subbasins identified as most susceptible to flooding. Cluster 3 encompasses the subbasins falling in the “Moderate” and “Least” categories with respect to the risk of flooding. The subbasin 12 (< 1 km2) exhibits a deviant morphometric pattern likely due to its specific topographical and network characteristics. The study reveals that cluster algorithms are effective in ranking and prioritizing subbasins of a river based on their potential for natural hazards like flooding. Moreover, the DBSCAN averts the use of cluster validation indices to determine the optimum clusters without compromising the results. All these methods would be beneficial in chalking out suitable management measures for different subbasins of a river based on their potential for any given hazard.https://doi.org/10.1111/jfr3.70079cluster algorithmsfloodingMahemorphometric parameterssubbasins |
| spellingShingle | Ajith G. Nair R. Kiran Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms Journal of Flood Risk Management cluster algorithms flooding Mahe morphometric parameters subbasins |
| title | Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms |
| title_full | Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms |
| title_fullStr | Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms |
| title_full_unstemmed | Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms |
| title_short | Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using Cluster Algorithms |
| title_sort | classification and flooding potential assessment of subbasins of a tropical river using cluster algorithms |
| topic | cluster algorithms flooding Mahe morphometric parameters subbasins |
| url | https://doi.org/10.1111/jfr3.70079 |
| work_keys_str_mv | AT ajithgnair classificationandfloodingpotentialassessmentofsubbasinsofatropicalriverusingclusteralgorithms AT rkiran classificationandfloodingpotentialassessmentofsubbasinsofatropicalriverusingclusteralgorithms |