Real-Time Detection of River Contaminants Using Neural Networks: A Case Study of the Ebro River

According to the UN World Water Development Report 2024, global food production has more than doubled over the past three decades, placing increasing pressure on freshwater systems due to climate change, urban expansion, and intensified pollution events. This study presents a Monitoring and Mitigation Framework (MMF) for real-time river contamination detection, contamination source identification, and estimation of Chemical Oxygen Demand (COD) concentrations at the source. The framework is based on Inverse Estimation (IE) algorithms using feed-forward neural networks trained on approximately 85,000 simulated pollution events for the Ebro River (Spain). Each event represents a 52 h contamination episode monitored at two locations with a 10 min sampling interval, covering a wide range of COD concentrations. For low-concentration scenarios (<1000 mg/L), the TensorFlow-based regression model achieved a Mean Absolute Relative Error (MARE) of 0.26% and a Mean Square Relative Error (MSRE) of 1.82%, while for higher concentrations (>1000 mg/L), the scikit-learn implementation provided superior performance with MARE below 1.85%. Source location identification achieved an accuracy of 81%, increasing to 97% when allowing adjacent river sections. Overall, the MMF is a scalable, low-cost, real-time decision-support tool for water authorities such as the Confederación Hidrográfica del Ebro (CHE) to detect, track, and mitigate pollution events.