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The transport and fate of admixtures at coastal zones are driven, or at least modulated, by currents. In particular, in tide-dominated areas due to higher near-bottom shear stress at strong currents, sediment concentration and turbidity are expected to be at maximum during spring tide, while algal growth rate likely is peaking up at slack currents during neap tide. Varying weather and atmospheric conditions might modulate the said dependencies, but the water quality pattern still is expected to follow the dominant tidal cycle. As tidal cycling could be predicted well ahead, there is a possibility to use water quality and hydrodynamic high-resolution data to learn past dependencies, and then use tidal hydrodynamic model for nowcasting and forecasting of selected water quality parameters. This paper develops data driven models for nowcasting and forecasting turbidity and chlorophyll-a using Artificial Neural Network (ANN) combined with Genetic Algorithm (GA). The use of GA aims to automate and enhance ANN designing process. The training of the ANN model is done by constructing input–output mapping, where hydrodynamic parameters act as an input for the network, while turbidity and chlorophyll-a are the corresponding outputs (desired target). Afterward, the prediction is carried out only by employing computed water surface elevation as an input for the trained ANN model. The proposed data driven model has successfully revealed complex relationships and utilized its experiential knowledge acquired from the training process for facilitating the subsequent use of the data driven model to yield an accurate prediction.

The chapter describes the development of a payload-carrying capability on the Folaga vehicles (eFolaga). In the eFolaga design, lightweight, small dimensions, low-cost characteristics have all been maintained, as well as high manoeuvrability and hovering capacities. A general methodology to derive lumped parameter models of eFolaga like vehicles has been described, where the identification of the eFolaga buoyancy change and mass displacement actuators has also been reported. By judicious design, it is possible to lift the eFolaga modularity also at the software level, and indeed to have intelligent payloads implementing specific autonomous behaviours setting up a dialogue with the native eFolaga GNC.