Study on the Framework for Real time Total Suspended Solids Monitoring using Sentinel-2 and Edge Artificial Intelligence

Monitoring total suspended solids (TSS) is critical for understanding water quality and managing pollution in river ecosystems. However, traditional methods face challenges in achieving real-time estimates in resource-constrained environments. This study aims to develop an optimized framework for convolutional neural network (CNN) to estimate TSS concentrations using Sentinel-2 multispectral data, with a focus on lightweight architecture and quantization techniques for real-time applications. Neural Architecture Search (NAS) combined with Pareto optimization was used to identify lightweight CNN models, ensuring high performance with minimal computational cost. Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) were applied to further compress model sizes while maintaining accuracy. Performance was evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) and Root Mean Squared Error (RMSE).

As a result, the lightweight Mobilenet (8.11 MB) attained an NSE of 0.828, and quantization further reduced the model size by 91%, yielding a compact 0.74 MB model with an enhanced NSE of 0.832. This quantized TSS estimation model showed the potential for real-time TSS estimation on mobile and edge devices. The proposed lightweighting and quantization framework provides a scalable solution for real-time TSS monitoring, connecting advanced machine learning methods with practical environmental applications. This approach enables efficient, real-time water quality assessment in a variety of environmental conditions, making it suitable for use on resource-constrained platforms such as drones, unmanned aerial vehicles and satellites.