Desmond Evurani

Dear colleague,

The development of an integrated, low-cost early warning system for dam safety is highly relevant and timely, especially in the context of climate change, extreme precipitation events, and the general degradation of hydraulic infrastructure in many regions. The combination of remote sensing technologies, IoT-based sensor networks, and machine learning algorithms offers great potential for building predictive and adaptive systems that go far beyond the limitations of traditional monitoring and inspection methods.

The key to success lies in the effective integration of multiple data sources—including satellite imagery (e.g., Sentinel-1 and 2), drones, and field-based sensors (e.g., for soil moisture, water pressure, ground displacement)—into a unified information system. Within this system, anomaly detection and behavior prediction algorithms (such as time-series models or neural networks) can enable early identification of potential structural issues.

For developing regions, where budgets are often limited, the most viable approach is to use low-cost and scalable technologies: open-source software tools, low-power microcontrollers (e.g., Arduino, Raspberry Pi), wireless communication networks (e.g., LoRa, NB-IoT), and cloud platforms for data processing and visualization. Engaging local engineering and academic capacity in system development and maintenance is crucial for long-term sustainability and local ownership.

This approach not only strengthens dam safety monitoring, but also contributes more broadly to disaster risk reduction (DRR) and sustainable water resource management, while reducing reliance on slow, expensive, and often subjective manual inspection methods.

I strongly recommend an intersectoral and transdisciplinary approach in the design and validation of such systems, ideally starting with pilot projects that can serve as scalable models for broader implementation.