Research Article
Prognosticate the Analogous Region in Bangladesh Utilizing an Unsupervised Machine Learning Technique
Md. Habibur Rahman*
,
Humayra Sadia
Issue:
Volume 11, Issue 3, June 2025
Pages:
46-62
Received:
28 March 2025
Accepted:
20 May 2025
Published:
13 June 2025
DOI:
10.11648/j.ijdsa.20251103.11
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Views:
Abstract: Climate regionalization provides valuable insights into the climatic challenges faced by a country, enabling better preparedness for climate change impacts and the development of targeted strategies. In this study, the climate regionalization of Bangladesh was performed based on nine climatic factors from 34 weather stations using unsupervised machine learning techniques. The exploratory data analysis was performed to assess the characteristics of the parameters, revealing distributional patterns. Principal Component Analysis (PCA) was then applied to reduce the dimensionality of the data and extract significant climate patterns. Following this, the non-hierarchical k-means clustering algorithm was used to group the locations into homogeneous clusters. The optimal number of clusters was determined using three widely recognized methods: the average silhouette score, the gap statistic, and the elbow method, before applying the clustering. While both the Silhouette Method and Gap statistic suggested three clusters, the elbow method identified nine clusters, which provided a more detailed regionalization. The locations Barisal, Jessore, Khepupara, Khulna, Mongla, Potuakhali, Satkhira from the south-west region form a significant cluster with Faridpur. The second largest cluster includes Bogra, Dinajpur, Ishurdi, Rajshahi, Rangpur, and Saidpur from the North-West region of Bangladesh. The findings of this study demonstrate that clustering offers a systematic approach to understanding the spatial distribution of climatic characteristics, facilitating informed decision making, resource allocation, and the development of policies tailored to the specific needs of different geographic regions in Bangladesh.
Abstract: Climate regionalization provides valuable insights into the climatic challenges faced by a country, enabling better preparedness for climate change impacts and the development of targeted strategies. In this study, the climate regionalization of Bangladesh was performed based on nine climatic factors from 34 weather stations using unsupervised mach...
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