Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data

•A deep learning model is used to predict chlorophyll concentration in two rivers.•Open-access hydrophysical features are used in addition to meteorological ones.•The federated learning approach can enhance the generalization of the models.•Low frequency data can be used to predict high frequency data.•The federated learning approach is suited when the data cannot be shared. Monitoring the concentration of pigments like chlorophyll (Chl) in water-bodies is a key task to contribute to their conservation. However, with the existing sensor technology, measurement in real-time and with enough frequency to ensure proper risk management is not completely feasible. In this work, with the concept of data-driven soft-sensing, three hydrophysical features are used together with three meteorological ones to estimate the concentration of Chl in two tributaries of the River Thames. Data driven models, specifically neural networks, are used with three learning approaches: individual, centralized and federated. Data reduction scenarios are proposed in order to analyze the performance of each approach when less data is available. The best results in the training are usually obtained with the individual approach. However, the federated learning provides better generalization ability. It was also observed that in most of the cases the results of the federated learning approach improve those of the centralized one.