doi: 10.18178/ijesd.2024.15.2.1470
Clustering Provinces with Drought Risk Based on Daily Maximum Temperature
A'yunin Sofro1,*
,
Elok Rizqi Auliya1 ,
Khusnia Nurul Khikmah1 ,
Danang Ariyanto1 ,
Riska Wahyu Romadhonia1 ,
Hernando Ombao2
- 1Mathematics Department, Faculty of Mathematics and Natural Sciences, Universitas Negeri Surabaya, East Java, 60231 Indonesia
- 2Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- Manuscript receivedSeptember 20, 2023
- revisedNovember 13, 2023
- acceptedDecember 21, 2023
- publishedMarch 22, 2024
Abstract
Changes in global weather patterns are sweepingthe world, including Indonesia. One of the causes of this changewas the El Niño event, where sea surface temperatures in thecentral Pacific Ocean experienced an increase. Apart fromcausing temperatures to increase, it also causes the intensity ofrainfall to decrease, causing drought disasters. Anticipatingnatural disasters and disaster mitigation needs to be carried outto reduce their negative impacts. Efforts can be made byidentifying areas with a high potential for drought andclustering areas based on the level of potential drought. Thisarticle focuses on extreme data from maximum temperatures in34 provinces in Indonesia. Clustering was performed using thek-means and k-medoids methods and evaluated using theDavies-Bouldin index. Predict the highest maximumtemperature in a specific period using the return level. Theresult shows that the k-means method is more suitable andbetter implemented by checking on the Davies-Boulding index,which is 0.9945.
Keywords
- clustering
- drought
- k-means
- k-medoids
- maximum temperature
How to Cite
A'yunin Sofro, Elok Rizqi Auliya, Khusnia Nurul Khikmah, Danang Ariyanto, Riska Wahyu Romadhonia, and Hernando Ombao, "Clustering Provinces with Drought Risk Based on Daily Maximum Temperature," International Journal of Environmental Science and Development, vol. 15, no. 2, pp. 69-76, 2024. https://doi.org/10.18178/ijesd.2024.15.2.1470
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).