Identification and clustering of drought-prone areas based on geographical, climatic, and socio-economic indicators: Supporting sustainable environmental management policies
DOI:
https://doi.org/10.61511/sudeij.v3i1.2026.3150Keywords:
drought, clustering analysis, sustainable environmental managementAbstract
Background: Drought is one of the most crucial environmental issues with widespread impacts across various regions of the world, including Indonesia, where changes in rainfall patterns and land use exacerbate the condition. This study aims to identify and cluster drought-prone areas in Central Sulawesi based on geographic, climatic, and socio-economic indicators, with 22 variables representing these three indicators. Methods: The analysis uses a quantitative approach based on data mining through the K-Means Clustering technique. Secondary data from 2019 to 2025 were integrated from multiple agencies, including BPS, BMKG, and BNPB, while the optimal number of clusters was determined using the Silhouette method executed via RStudio. Findings: The analysis results show three clusters with different levels of drought vulnerability, namely Cluster 1 (high drought), Cluster 2 (moderate drought), and Cluster 3 (low drought). Cluster 1 is characterized by high temperatures, low rainfall, and intensive mining activities. Cluster 2 has moderate rainfall and better environmental conditions. Cluster 3 shows relatively stable hydrological and socio-economic conditions. Climatic factors, particularly rainfall are the most influential indicators of drought vulnerability. Geographical factors such as irrigated areas and the extent of forests and water bodies also contribute, as do socioeconomic factors such as population density, poverty levels, and access to clean water. Conclusion: This analysis provides a spatial overview of the distribution of drought risk and serves as a scientific basis for policy formulation. The analysis then provides policy recommendations, including irrigation development and water conservation in moderately vulnerable areas, sustainable resource management in low-risk areas, and green economy development in safe areas to support sustainable environmental management. Novelty/Originality of this article: The novelty of this study lies in its integrated multidimensional approach, combining geographic, climatic, and socio-economic indicators through K-Means Clustering to map drought vulnerability in Central Sulawesi.
References
Akhmad, R., Sumarmi, S., Astina, I. K., & Wagistina, S. (2025). A sustainability trilogy approach for drought risk prevention: Case study in Indonesia. Jàmbá Journal of Disaster Risk Studies, 1–12. https://doi.org/10.4102/jamba.v17i1.1811
Alfiandy, S., & Permana, D. S. (2020). Trend of Rainfall Based on BMKG Observation Data and MERRA-2 NASA Reanalysis in Central of Sulawesi Province. Jurnal Sains & Teknologi Modifikasi Cuaca, 21(2), 63–72. https://ejournal.brin.go.id/JSTMC/article/view/1205
Alfonso, L., Martos, P., García-vico, Á. M., González, P., & Carmona, C. J. (2023). Clustering : an R library to facilitate the analysis and comparison of cluster algorithms. Progress in Artificial Intelligence, 12(1), 33–44. https://doi.org/10.1007/s13748-022-00294-2
Awad, F. H., & Hamad, M. M. (2022). Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor. Electronics (Switzerland), 11(6). https://doi.org/10.3390/electronics11060883
Badan Metereologi Klimatologi Geofisika. (2025). Iklim Sulawesi Tengah. Badan Metereologi Klimatologi Geofisika. https://www.bmkg.go.id
Badan Nasional Penanggulangan Bencana. (2025). PUSDALOPS-PB SULTENG. Badan Nasional Penanggulangan Bencana. https://www.dashboard.core.pusdalops-bpbdsulteng.com
Badan Pusat Statistik Sulawesi Tengah. (2025). Sulawesi Tengah Dalam Angka 2025 (BPS Sulawesi Tengah (Ed.); Vol. 55). BPS Sulawesi Tengah. https://sulteng.bps.go.id
Bahtiar, A., & Yulfiah, Y. (2025). Impacts of La Niña on Sea Surface Temperature, Chlorophyll-a, and Fishery Productivity in Northern East Java Ocean. Journal of Earth and Marine Technology (JEMT), 5(2), 171–183. https://doi.org/10.31284/j.jemt.2025.v5i2.7905
Biswas, A., Sarkar, S., Das, S., Dutta, S., Roy Choudhury, M., Giri, A., Bera, B., Bag, K., Mukherjee, B., Banerjee, K., Gupta, D., & Paul, D. (2025). Water scarcity: A global hindrance to sustainable development and agricultural production – A critical review of the impacts and adaptation strategies. Cambridge Prisms: Water, 3. https://doi.org/10.1017/wat.2024.16
Bulanov, N., Suvorov, A. Y., Blyuss, O. B., Munblit, D. B., Butnaru, D. V., Nadinskaia, M. Y., & Zaikin, A. A. (2021). Basic principles of descriptive statistics in medical research. Sechenov Medical Journal, 12(3), 4–16. https://doi.org/10.47093/2218-7332.2021.12.3.4-16
Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D. L. R., Thompson, E. B., & Ashraf, I. (2023). A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective. Symmetry, 15(9), 1–44. https://doi.org/10.3390/sym15091679
Cuevas-Diaz Duran, R., Wei, H., & Wu, J. (2024). Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets. BMC Genomics, 25(1), 1–18. https://doi.org/10.1186/s12864-024-10364-5
Diggle, P. J. (2023). Data sets. Time Series, 228–244. https://doi.org/10.1093/oso/9780198522065.005.0001
Dinas Energi dan Sumber Daya Mineral Sulawesi Tengah. (2025). Buku Data dan Statistik Energi dan Sumber Daya Mineral Sulawesi Tengah (DESDM Sulteng (Ed.)). https://desdm.sultengprov.go.id
Doan, Q. Van, Amagasa, T., Pham, T. H., Sato, T., Chen, F., & Kusaka, H. (2023). Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data. Geoscientific Model Development, 16(8), 2215–2233. https://doi.org/10.5194/gmd-16-2215-2023
En-Nagre, K., Aqnouy, M., Ouarka, A., Ali Asad Naqvi, S., Bouizrou, I., Eddine Stitou El Messari, J., Tariq, A., Soufan, W., Li, W., & El-Askary, H. (2024). Assessment and prediction of meteorological drought using machine learning algorithms and climate data. Climate Risk Management, 45(October 2023), 100630. https://doi.org/10.1016/j.crm.2024.100630
Faizan, M., Zuhairi, M. F., Ismail, S., & Sultan, S. (2020). Applications of Clustering Techniques in Data Mining: A Comparative Study. International Journal of Advanced Computer Science and Applications, 11(12), 146–153. https://doi.org/10.14569/IJACSA.2020.0111218
Farkas, G. (2019). Possibilities of using raster data in client‐side web maps. Transactions in GIS, 24(1). https://doi.org/10.1111/tgis.12588
Gallego, V., Freixes, A., & Lingan, J. (2025). Applying Machine Learning in Marketing: An Analysis Using the NMF and K-Means Algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 14778 LNCS, 14–26. https://doi.org/10.1007/978-3-031-78238-1_2
Hartama, D., & Oktaviani, S. (2025). Optimization of K-Means and K-Medoids Clustering Using Dbi Silhouette Elbow on Student Data. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 11(2), 289–296. https://doi.org/10.33330/jurteksi.v11i2.3531
Iqbal, A., Shil, A., Chowdhury, M. J. M., Moni, M. A., & Sarker, I. H. (2022). An Improved K-means Clustering Algorithm Towards an. Annals of Data Science. https://doi.org/10.1007/s40745-022-00428-2
Juliannisa, I. A., Rahma, H., Mulatsih, S., & Fauzi, A. (2025). Regional Vulnerability to Food Insecurity: The Case of Indonesia. Sustainability (Switzerland), 17(11), 1–20. https://doi.org/10.3390/su17114800
Kuswanto, H., Puspa, A. W., Ahmad, I. S., & Hibatullah, F. (2021). Drought Analysis in East Nusa Tenggara (Indonesia) Using Regional Frequency Analysis. Scientific World Journal, 2021. https://doi.org/10.1155/2021/6626102
Li, M., Frank, E., & Pfahringer, B. (2023). Large scale K-means clustering using GPUs. Data Mining and Knowledge Discovery, 37(1), 67–109. https://doi.org/10.1007/s10618-022-00869-6
Lim, Z. Y., Ong, L. Y., & Leow, M. C. (2021). A review on clustering techniques: Creating better user experience for online roadshow. Future Internet, 13(9). https://doi.org/10.3390/fi13090233
Mason, L., Hicks, B., & Almeida, J. S. (2025). ClusterRadar: An interactive web-tool for the multi-method exploration of spatial clusters over time. PLoS ONE, 20(5 May), 1–23. https://doi.org/10.1371/journal.pone.0322393
Mens, M. J. P., Van Rhee, G., Schasfoort, F., & Kielen, N. (2022). Integrated drought risk assessment to support adaptive policymaking in the Netherlands. Natural Hazards and Earth System Sciences, 22(5), 1763–1776. https://doi.org/10.5194/nhess-22-1763-2022
Minea, I., & Albulescu, A. C. (2025). Drought Over Time: an Investigation of the Impact of Meteorological Drought on Groundwater and Surface Water in the East of Romania. Earth Systems and Environment, 9(3), 1959–1981. https://doi.org/10.1007/s41748-025-00725-9
Mu’arif, Z., Afriza, D. A., Aulia, F., Anggelina E, M. P., & Gamayanti, N. F. (2024). Analysis of Priority Areas for Handling Stunting Cases in Sigi Regency Using the Topsis Method Based on Web Dashboard. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 18(3), 1411–1422. https://doi.org/10.30598/barekengvol18iss3pp1411-1422
Mutambik, I. (2024). An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams. Sensors, 24(22). https://doi.org/10.3390/s24227412
Mutia, R., Mardani, M., Fathia, S., & Kusmita, T. (2023). Identifikasi Sesar dengan Menggunakan Metode Gaya Berat (Studi Kasus: Sulawesi Tengah). Jurnal Riset Fisika Indonesia, 3(2), 1–6. https://doi.org/10.33019/jrfi.v3i2.3552
Orimoloye, I. R., Belle, J. A., Orimoloye, Y. M., Olusola, A. O., & Ololade, O. O. (2022). Drought: A Common Environmental Disaster. Atmosphere, 13(1). https://doi.org/10.3390/atmos13010111
Oyewole, G. J., & Thopil, G. A. (2023). Data clustering: application and trends. In Artificial Intelligence Review (Vol. 56, Issue 7). Springer Netherlands. https://doi.org/10.1007/s10462-022-10325-y
Pant, Y. R., Leigh, L., & Fajardo Rueda, J. (2025). Improving K-Means Clustering: A Comparative Study of Parallelized Version of Modified K-Means Algorithm for Clustering of Satellite Images. Algorithms, 18(8). https://doi.org/10.3390/a18080532
Roy, S. K., Morshed, A., Mojumder, P., Hasan, M. M., & Islam, A. K. M. S. (2024). Innovative trend analysis technique with fuzzy logic and K-means clustering approach for identification of homogenous rainfall region: A long-term rainfall data analysis over Bangladesh. Quaternary Science Advances, 15(June), 100227. https://doi.org/10.1016/j.qsa.2024.100227
Sanjari, E., Majidian Dehkordi, F., & Raeisi Shahraki, H. (2023). Clustering Undergraduate Students Based on Academic Burnout and Satisfaction from the Field Using Partitioning around Medoid. Computational and Mathematical Methods in Medicine, 2023(c). https://doi.org/10.1155/2023/8898939
Savelli, E., Rusca, M., Cloke, H., & Di Baldassarre, G. (2022). Drought and society: Scientific progress, blind spots, and future prospects. Wiley Interdisciplinary Reviews: Climate Change, 13(3), 1–25. https://doi.org/10.1002/wcc.761
Shim, I., Kim, H., Hong, B., An, J., & Hwang, T. (2021). Drought vulnerability assessment and cluster analysis of island areas taking Korean island areas at Eup (Town) and Myeon (subcounty) levels as study targets. Water (Switzerland), 13(24). https://doi.org/10.3390/w13243657
Starbuck, C. (2023). The Fundamentals of People Analytics. In The Fundamentals of People Analytics. https://doi.org/10.1007/978-3-031-28674-2
Tabianan, K., Velu, S., & Ravi, V. (2022). K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability (Switzerland), 14(12), 1–15. https://doi.org/10.3390/su14127243
Van Loon, A. F., Kchouk, S., Matanó, A., Tootoonchi, F., Alvarez-Garreton, C., Hassaballah, K. E. A., Wu, M., Wens, M. L. K., Shyrokaya, A., Ridolfi, E., Biella, R., Nagavciuc, V., Barendrecht, M. H., Bastos, A., Cavalcante, L., de Vries, F. T., Garcia, M., Mård, J., Streefkerk, I. N., … Werner, M. (2024). Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems. Natural Hazards and Earth System Sciences, 24(9), 3173–3205. https://doi.org/10.5194/nhess-24-3173-2024
Wang, Q., Yang, X., Qu, Y., Qiu, H., Wu, Y., Qi, J., Song, H., Chen, Y., Chu, H., & Zeng, J. (2025). Global Climate Change Exacerbates Socioeconomic Drought Severity Across Vegetation Zones During 1901–2018. International Journal of Disaster Risk Science, 16(2), 291–306. https://doi.org/10.1007/s13753-025-00631-8
Wani, A. A. (2024). Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions. PeerJ Computer Science, 10, 1–45. https://doi.org/10.7717/PEERJ-CS.2286
Wijitkosum, S. (2025). Integrated spatial analysis of drought risk factors using agglomerative hierarchical clustering and correlation. Environmental Advances, 21(June), 100646. https://doi.org/10.1016/j.envadv.2025.100646
Yang, J., Wang, Y. K., Yao, X., & Lin, C. T. (2021). Adaptive Initialization Method for K-Means Algorithm. Frontiers in Artificial Intelligence, 4(November), 1–13. https://doi.org/10.3389/frai.2021.740817
Yang, X., Liao, X., Di, D., & Shi, W. (2023). A Review of Drought Disturbance on Socioeconomic Development. Water (Switzerland), 15(22). https://doi.org/10.3390/w15223912
Yin, H., Aryani, A., Petrie, S., Nambissan, A., Astudillo, A., & Cao, S. (2024). A Rapid Review of Clustering Algorithms. 1–25. http://arxiv.org/abs/2401.07389
Yousif, A., Drou, N., Rowe, J., Khalfan, M., Gunsalus, K. C., & Gunsalus, K. C. (2020). NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization. BMC Bioinformatics, 21(1), 1–14. https://doi.org/10.1186/s12859-020-03577-4
Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., & Kycko, M. (2021). Comparison of random forest, support vector machines, and neural networks for post-disaster forest species mapping of the krkonoše/karkonosze transboundary biosphere reserve. Remote Sensing, 13(13). https://doi.org/10.3390/rs13132581
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