50 Years of Water Body Monitoring: The Case of Qaraaoun Reservoir, Lebanon

2025-10-28

AI Summary

To address unsustainable reservoir storage monitoring at Lebanon’s Qaraoun Reservoir—caused by frequent ground sensor failures and limited operational capacity—this study proposes a sensor-free remote sensing approach. It fuses Sentinel-2 and Landsat imagery to develop a novel water segmentation index for high-accuracy water surface extraction (>95% accuracy), and integrates support vector regression (SVR) with GridSearchCV hyperparameter optimization to establish an area–volume relationship model calibrated against bathymetric data. Relying solely on open-access satellite data, the method enables near-real-time storage estimation with <1.5% volumetric error and R² > 0.98, successfully reconstructing a reliable 50-year time series. Its core innovation is an end-to-end, sensorless remote sensing inversion framework that exhibits strong robustness, high accuracy, and cross-regional transferability—providing a scalable technical paradigm for sustainable reservoir management in data-scarce regions.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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 surface area and volume in near real time. Sentinel-2 and Landsat 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 calculations using a reservoir bathymetry survey. The model is then able to estimate reservoir volume relying solely on surface area extracted from satellite imagery, without the need for ground measurements. Water segmentation using the proposed index aligns with ground truth for more than 95 percent of the shoreline. Hyperparameter tuning with GridSearchCV yields an optimized SVR performance with error under 1.5 percent of full reservoir capacity and coefficients of determination exceeding 0.98. These results demonstrate the robustness and cost-effectiveness of the method, offering a practical solution for continuous, sensor-independent monitoring of reservoir storage. The proposed methodology can be replicated for other water bodies, and the resulting 50 years of time-series data is valuable for research on climate change and environmental patterns.

Problem

• Monitoring reservoir volume without sensors using satellite imagery
• Estimating water storage with machine learning and water segmentation
• Providing continuous reservoir monitoring despite sensor malfunctions

Innovation

• Sensor-free approach using open-source satellite imagery
• Machine learning model estimates volume from surface area
• Proposed water segmentation index achieves high accuracy