The sustainable management of the Qaraaoun Reservoir, the largest surface water body in Lebanon located in the Bekaa Plain, depends on reliable monitoring of its storage volume despite frequent sensor malfunctions and limited maintenance capacity. This study introduces a sensor-free approach that integrates open-source satellite imagery, advanced water-extent segmentation, and machine learning to estimate the reservoir's surface area and, subsequently, its volume in near real time. Sentinel-2 and Landsat 1-9 images are processed, where surface water is delineated using a newly proposed water segmentation index. A machine learning model based on Support Vector Regression (SVR) is trained on a curated dataset that includes water surface area, water level, and water volume derived from a reservoir bathymetric survey. The model is then able to estimate the water body's volume solely from the extracted water surface, without the need for any ground-based measurements. Water segmentation using the proposed index aligns with ground truth for over 95% of the shoreline. Hyperparameter tuning with GridSearchCV yields an optimized SVR performance, with an error below 1.5% of the full reservoir capacity and coefficients of determination exceeding 0.98. These results demonstrate the method's robustness and cost-effectiveness, offering a practical solution for continuous, sensor-independent monitoring of reservoir storage. The proposed methodology is applicable to other water bodies and generates over five decades of time-series data, offering valuable insights into climate change and environmental dynamics, with an emphasis on capturing temporal trends rather than exact water volume measurements.

翻译：卡拉翁水库是黎巴嫩贝卡平原最大的地表水体，其可持续管理依赖于对其蓄水量的可靠监测，尽管传感器频繁故障且维护能力有限。本研究提出了一种无需传感器的监测方法，该方法整合了开源卫星影像、先进的水域分割技术和机器学习，以近乎实时地估算水库的表面积及其蓄水量。研究处理了Sentinel-2和Landsat 1-9影像，其中地表水通过新提出的水域分割指数进行划定。基于支持向量回归（SVR）的机器学习模型在一个精心构建的数据集上训练，该数据集包括从水库水深测量中获取的水表面积、水位和蓄水量。随后，该模型仅从提取的水表面积即可估算水体蓄水量，无需任何地面测量。使用所提指数进行的水域分割与地面真实数据在超过95%的岸线区域保持一致。通过GridSearchCV进行超参数调优，优化了SVR性能，其误差低于水库总容量的1.5%，且决定系数超过0.98。这些结果证明了该方法的稳健性和成本效益，为水库蓄水量提供了一种连续、不依赖传感器的实用监测方案。所提方法适用于其他水体，并生成了超过五十年的时间序列数据，为气候变化和环境动态提供了宝贵见解，重点在于捕捉时间趋势而非精确的蓄水量测量。