Emergency Management

Emergency Management

Investigating the Impact of Climate Regimes in Areas Surrounding the Persian Gulf and Gulf of Oman on the Persian Gulf Water Body: A Multi-Algorithm Unsupervised Machine Learning Approach

Document Type : Original Article

Authors
Alireza Khadem Daghigh 1
Ali Zali 1
Elaheh Asgari 2
1 Dept. of Computer Science, Faculty of Mathematics and Computer Science, Iran University of Science and Technology, Tehran, Iran
2 Postdoctoral in Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
Abstract
As a shallow, semi-enclosed sea, the Persian Gulf is particularly sensitive to climate change and coupled atmospheric-oceanic variability. However, the role of climate regimes in the surrounding regions including adjacent coastal lands, neighboring shorelines, and the Gulf of Oman in modulating or amplifying the thermal characteristics of the Gulf’s water body has not yet been comprehensively investigated. This study employs a multi-algorithm unsupervised machine learning framework to examine the influence of surrounding climate regimes on the thermal dynamics of the Persian Gulf over the period 1960-2021. ERA5 reanalysis data encompassing key atmospheric and oceanic variables (temperature, wind, precipitation, evaporation, radiation, and cloud cover) were extracted, standardized, and analyzed using five complementary clustering algorithms: K-Means, MiniBatchKMeans, Gaussian Mixture Models (GMM), DBSCAN, and Fuzzy C-Means. The outputs were integrated through consensus clustering, resulting in the identification of five robust climate regimes with distinct impacts on the Gulf’s sea surface temperature (SST). The results indicate that the northern extreme-heat regime is strongly associated with sustained SST increases in the shallow northern Gulf, whereas the southern marine-moderate and monsoon-fringe regimes exert a pronounced thermal-moderating effect. Teleconnection analysis reveals that the Indian Ocean Dipole (IOD) is the dominant driver of wet winter anomalies, while ENSO primarily governs the intensification of summer monsoonal intrusions. By introducing a data-driven and transferable framework, this study provides new insights into regime-based SST predictability, ecosystem risk assessment, and climate-resilient management strategies for semi-enclosed seas.
Keywords
Climate Regimes, El Niñ
o-Southern Oscillation (ENSO), Unsupervised Machine Learning, Indian Ocean Dipole (IOD), Consensus Clustering, Teleconnections, Persian Gulf
Subjects
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Emergency Management
Volume 15, Issue 1 - Serial Number 33
Serial number 33, Spring 1405
Spring 2026
Pages 38-61

  • Receive Date 22 November 2025
  • Revise Date 20 December 2026
  • Accept Date 07 January 2026