دانشگاه بیرجند-گروه پژوهشی خشکسالی وتغییراقلیم مجله پژوهش های خشکسالی و تغییراقلیم 3092-6076 3 4 2026 02 20 Assessing GPR and SVM Performance with Uncertainty Analysis for Drought Prediction in Iran's Diverse Climate Regions Assessing GPR and SVM Performance with Uncertainty Analysis for Drought Prediction in Iran's Diverse Climate Regions 125 152 3666 10.22077/jdcr.2025.9952.1164 FA Mehrnaz Yahyazadeh Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources Engineering, University of Hormozgan, Bandarabbas, Iran. 0009-0008-4039-3263 Ommolbanin Bazrafshan Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources Engineering, University of Hormozgan, Bandarabbas, Iran. 0000-0003-2524-3992 Navazollah Moradi Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, BandarAbbas, Iran. 0000-0002-6588-9440 Hossein Zamani Department of Mathematics and Statistics, Faculty of Science, University of Hormozgan, Bandarabbas, Iran. 0000-0003-1126-6288 Mehdi Biniaz Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources Engineering, University of Hormozgan, Bandarabbas, Iran. Journal Article 2025 08 21 This study presents a comprehensive evaluation of Support Vector Machine (SVM) and Gaussian Process Regression (GPR) models for drought prediction across Iran's diverse climate zones using the Standardized Precipitation-Evapotranspiration Index (SPEI). The research integrates teleconnection indices, satellite data, and machine learning to address limitations of traditional drought forecasting methods. Results demonstrate the superior performance of GPR with Laplace kernel, achieving higher accuracy (R²: 0.91-0.75 in training, 0.85-0.37 in testing) and better uncertainty quantification (UA: 1.12-2.33, PICP: 1.0) compared to SVM-RBF. This practical improvement translates to a 10-15% increase in the explained variance of drought intensity, a critical distinction for activating different levels of emergency response. The Laplace kernel's flexibility in modeling abrupt climatic variations and GPR's probabilistic framework provide more reliable drought forecasts, particularly in extreme climates. Random Forest analysis revealed distinct climatic drivers, with temperature and evapotranspiration dominating arid regions, while oceanic oscillations (ENSO, WHWP) controlled humid zones. The UNEEC method provided robust uncertainty assessment, showing GPR's consistent performance across different climate classifications. While SVM-RBF remained competitive in moderate climates, its accuracy declined in complex conditions. The findings highlight GPR's advantages for precision drought forecasting in operational early warning systems, where reliable probabilistic forecasts can optimize reservoir management and agricultural advisory services, while acknowledging SVM's computational efficiency for large-scale monitoring applications. 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. https://jdcr.birjand.ac.ir/article_3666_1ea97de85eb634d580161c603422437f.pdf