Estimating Water Levels through Smartphone-Imaged Gauges: A Comparative Analysis of ANN, DL, and CNN Models

Understanding and monitoring water levels are essential for various applications, including environmental protection, public safety, and resource management. Water level estimation, a critical aspect of hydrological monitoring, is often constrained by challenges such as resource scarcity, high costs, and time-intensive processes. This research addresses these limitations by developing a machine learning-based system for automatic and real-time water level control. Specifically, it investigates the effectiveness of a non-contact, image-based water level measurement approach, leveraging recent advancements in mobile imaging technology. Images were captured using a standard smartphone equipped with an RGB camera for water level analysis. Through precise image alignment processing under both clear and turbid conditions, the water’s edge on a gauge was accurately detected. The study centers on the development and comparison of three computational models: Artificial Neural Networks (ANN), Deep Learning (DL), and Convolutional Neural Networks (CNN). These models were trained to estimate water levels from processed image data. Results demonstrated varying levels of accuracy across models, with the CNN model outperforming others, achieving the lowest error rate of 24.36 mm and the highest correlation of 0.986. In contrast, the ANN model yielded the highest error rate at 30.76 mm and the lowest correlation of 0.968, highlighting the relative effectiveness of CNN in this application. Given the high accuracy (92.6%) of the image processing method and CNN model in detecting water surface edges and determining water levels, this system has substantial potential to enhance water resource management and control efficiency.