Accurate water-level monitoring is vital for effective river management, flood prevention, and environmental conservation efforts. The increasing frequency and severity of flooding events, driven by climate change, highlight the critical need for reliable, robust, and sustainable water-level measurement systems. Such systems are particularly necessary in remote and resource-constrained settings where conventional infrastructure is lacking or insufficient.

Traditional water-level measurement methods, including radar, ultrasonic, pressure sensors, and conventional imaging systems, encounter significant limitations. These include high acquisition and maintenance costs, susceptibility to environmental damage, vandalism risks due to conspicuous placement, and dependence on stable, continuous power sources. Consequently, there remains an unmet demand for affordable, resilient, and autonomous monitoring devices capable of functioning reliably in challenging and off-grid environments.

To address this gap, this study presents the development and validation of an innovative water-level monitoring prototype as a prove of concept. The proposed system integrates a low-cost Raspberry Pi-based imaging sensor equipped with infrared illumination to enable accurate measurements both during the day and at night. An onboard processing unit autonomously captures and analyses images in real-time using one of two distinct image-processing algorithms: the mean-difference method and the Kolmogorov–Smirnov (KS) test method. These algorithms quantify water levels by detecting pixel-intensity contrasts, thus eliminating the need for direct physical contact with the water body.

The prototype underwent field validation in a natural river environment, demonstrating robust and consistent performance. The system achieved an accuracy within ±4.6 cm at a 95% confidence interval. Notably, it exhibited stable performance under varying environmental conditions, with moderate bias differences between daytime and nighttime scenarios, and minimal sensitivity to precipitation effects. The mean-difference algorithm demonstrated superior precision, while the KS-test method offered enhanced robustness against environmental variability.

This research underscores the practical feasibility and significant potential of low-cost, autonomous camera-based systems for sustainable water-level monitoring. Such solutions can substantially improve disaster preparedness and environmental management, especially in remote or economically disadvantaged regions lacking traditional monitoring infrastructure. Future research directions include integrating renewable energy solutions such as solar power to enhance operational autonomy, exploring advanced image-processing algorithms for increased accuracy, and conducting extended validations across a broader range of hydrological and environmental scenarios.