Enhancing estuary salinity prediction: A Machine Learning and Deep Learning based approach

As critical transitional ecosystems, estuaries are facing the increasingly urgent threat of salt wedge intrusion, which impacts their ecological balance as well as human-dependent activities. Accurately predicting estuary salinity is essential for water resource management, ecosystem preservation, and for ensuring sustainable development along coastlines. In this study, we investigated the application of different machine learning and deep learning models to predict salinity levels within estuarine environments. Leveraging different techniques, including Random Forest, Least-Squares Boosting, Artificial Neural Network and Long Short-Term Memory networks, the aim was to enhance the predictive accuracy in order to better understand the complex interplay of factors influencing estuarine salinity dynamics. The Po River estuary (Po di Goro), which is one of the main hotspots of salt wedge intrusion, was selected as the study area. Comparative analyses of machine learning models with the state-of-the-art physics-based Estuary box model (EBM) and Hybrid-EBM models were conducted to assess model performances. The results highlighted an improvement in the machine learning performance, with a reduction in the RMSE (from 4.22 psu obtained by physics-based EBM to 2.80 psu obtained by LSBoost-Season) and an increase in the R2 score (from 0.67 obtained by physics-based EBM to 0.85 by LSBoost-Season), computed on the test set. We also explored the impact of different variables and their contributions to the predictive capabilities of the models. Overall, this study demonstrates the feasibility and effectiveness of ML-based approaches for estimating salinity levels due to salt wedge intrusion within estuaries. The insights obtained from this study could significantly support smart management strategies, not only in the Po River estuary, but also in other location

Convolutional Neural Networks for Risso’s Dolphins Identification

Photo-identication is one of the best practices to estimate the abundance of cetaceans and, as such, it can help to obtain the biological information necessary to decision-making and actions to preserve the marine environment and its biodiversity. The Risso's dolphin is one of the least-known cetacean species on a global scale, and the distinctive scars on its dorsal n proved to be extremely useful to photo-identify single individuals. The main novelty of this paper is the development of a newmethod based on deep learning, called Neural Network Pool (NNPool), and specically devoted to the photo-identication of Risso's dolphins. This new method also includes the unique function of recognizing unknown vs known dolphins in large datasets with no interaction by the user. Moreover, the new version of DolFin catalogue, collecting Risso's dolphins data and photos acquired between 2013-2018 in the Northern Ionian Sea (Central-eastern Mediterranean Sea), is presented and used here to carry out the experiments. Results have been validated using a further data set, containing new images of Risso's dolphins from the Northern Ionian Sea and the Azores, acquired in 2019. The performance of the NNPool appears satisfying and increases proportionally to the number of images available, thus highlighting the importance of building large-scale data set for the application at hand

Machine Learning to predict cetacean behaviour using social and environmental features

Understanding how behaviour is influenced by the environment in which animals move and being able to predict it from social and environmental features is a very ambitious but necessary goal for their conservation. This is especially true for cetaceans, marine mammals with very complex social life and ecology which are nowadays facing various threats to their wellbeing and survival. To that aim, we propose a Machine Learning framework, based on Random Forest and RUSBoost algorithms, to predict cetacean behaviour from a plethora of 27 variables, including the group size and oceanographic features provided by Copernicus Marine Service (CMS). Models have been developed using behavioural data collected in the 2016-2021 period on striped, Risso’s and bottlenose common dolphins sighted in the Gulf of Taranto. The performance reached with the ML approach for the classification of feeding behaviour is remarkable, with a prediction accuracy achieved by the dedicated models of about 75%. Thus, the proposed strategy can be successfully implemented in future works to forecast target species behaviour and to investigate further the influence of anthropic variables and other habitat characteristics on it in order to enhance their conservation