Effective monitoring of water surface changes in reservoirs is crucial for water resource management, especially in arid and semi-arid regions. Traditional ground-based methods, although accurate, are labor-intensive and impractical for large-scale monitoring. Synthetic Aperture Radar (SAR) remote sensing offers a promising alternative by enabling continuous observation of water bodies. This study utilizes 360 ascending Sentinel-1 images in VV and VH polarizations to analyze water surface area changes at the Boukan embankment dam in western Iran. The Support Vector Machine (SVM) algorithm was applied for classification, while the Improved Atom Search Optimization-Extreme Learning Machine (IASO-ELM) model was used to integrate classification results with climatic data. The IASO-ELM model optimizes the initialization of weights and biases within the ELM framework, enhancing predictive accuracy. The model's performance was evaluated using several metrics, with Scenario 8 demonstrating the best results, including the lowest RMSE (1.8524), MAE (1.4698), and high R2 (0.9608), NSE (0.9608), and WI (0.9901), indicating strong agreement with observed data. Scenario 3 performed the worst, with the highest RMSE (3.0112) and MAE (2.2785), and a lower R2 (0.8983), showing weak predictive accuracy. A comparison between water surface areas derived from Sentinel-1-based SVM classification and those obtained using the NDWI index on Sentinel-2 imagery was also conducted. Results showed that Sentinel-1-based SVM classification provided more accurate results, with errors below 15 %, compared to NDWI's error of 35 % in June 2020. This highlights the superiority of classification-based methods over simple indices in capturing complex variations in water surface dynamics. The IASO-ELM model's ability to accurately predict water surface changes offers a robust tool for water management, supporting proactive strategies for flood prevention, drought mitigation, and sustainable water resource planning. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.