Assessing GPR and SVM Performance with Uncertainty Analysis for Drought Prediction in Iran's Diverse Climate Regions

Yahyazadeh, Mehrnaz; Bazrafshan, Ommolbanin; Moradi, Navazollah; Zamani, Hossein; Biniaz, Mehdi

doi:10.22077/jdcr.2025.9952.1164

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	Assessing GPR and SVM Performance with Uncertainty Analysis for Drought Prediction in Iran's Diverse Climate Regions
مجله پژوهش های خشکسالی و تغییراقلیم
دوره 3، شماره 4 - شماره پیاپی 12، اسفند 1404، صفحه 125-152 اصل مقاله (1.63 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22077/jdcr.2025.9952.1164
نویسندگان
Mehrnaz Yahyazadeh¹؛ Ommolbanin Bazrafshan^* ¹؛ Navazollah Moradi²؛ Hossein zamani³؛ Mehdi Biniaz¹
¹Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources Engineering, University of Hormozgan, Bandarabbas, Iran.
²Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, BandarAbbas, Iran.
³Department of Mathematics and Statistics, Faculty of Science, University of Hormozgan, Bandarabbas, Iran.
چکیده
This study evaluates Support Vector Machine (SVM) and Gaussian Process Regression (GPR) models for predicting drought in Iran using the Standardized Precipitation-Evapotranspiration Index (SPEI). Results demonstrate the superior performance of the GPR model with a Laplace kernel, which achieved higher predictive accuracy (R²: 0.85-0.37 in testing) and superior uncertainty quantification (PICP: 1.0) compared to the SVM-RBF model. GPR's flexibility in modeling abrupt climatic shifts and its probabilistic framework provide more reliable drought forecasts, especially in extreme climates. Random Forest analysis revealed key climatic drivers, with temperature and evapotranspiration dominating in arid regions, while oceanic oscillations (ENSO, WHWP) were more influential in humid zones. Although SVM remained competitive in moderate climates, GPR is recommended as the superior choice for operational early warning systems, enabling optimized water resource management and agricultural advisories through its reliable probabilistic forecasts. This research establishes a framework for integrating model accuracy and uncertainty assessment in drought prediction.
کلیدواژه‌ها
Drought Prediction؛ Machine Learning؛ Uncertainty Quantification؛ Teleconnection Patterns؛ UNEEC Method

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Assessing GPR and SVM Performance with Uncertainty Analysis for Drought Prediction in Iran's Diverse Climate Regions