Recurrent neural networks for water quality assessment in complex coastal lagoon environments: A case study on the Venice Lagoon

Abstract

Eutrophication represents an important ecological and environmental issue in coastal lagoons. This paper presents an extensive study of recurrent cell and network architectures to model eutrophication processes in the Venice lagoon, a very complex and fragile ecosystem that has been strongly altered by anthropic activities over years. Experimental results showed that recurrent models outperformed Random Forests (RF) significantly on two datasets, performing similarly to CNNs on one of the datasets, while outperforming CNNs on the other one. Additionally, the transferability potential of the trained models was investigated. The empirical analysis has shown that recurrent neural network models with lower computational complexity provide the highest eutrophication prediction accuracy when their trained models were tested on a new dataset. Designed models represent effective tools for early-warning eutrophication prediction that can support the implementation of relevant EU acquis (EU Marine Strategy and Water Framework Directives) and achievement of their environmental targets.