Digital H2O - "Using sensors, drones, and machine learning for real-time water quality analysis

Abstract: This research explores Digital H2O, an innovative system integrating sensors, drones, and machine learning for real-time water quality analysis. Traditional methods are often time-consuming, labour-intensive, and provide limited coverage. Digital H2O addresses these limitations by utilizing a network of low-cost, Internet of Things (IoT) sensors to collect in-situ water quality data, such as pH, turbidity, and dissolved oxygen levels. Drones equipped with multispectral cameras capture aerial imagery, providing a broader perspective and detecting potential pollution sources. Advanced machine learning algorithms analysed the sensor data and drone imagery, enabling accurate and rapid assessment of key water quality parameters. Digital H2O generates real-time water quality maps and alerts, facilitating timely intervention and effective water resource management. This integrated approach offers a cost-effective, efficient, and scalable solution for continuous water quality monitoring, contributing to environmental protection and public health