A Machine Learning Based Approach to Predict Ostreopsis Cf. Ovata Bloom Events from Meteo- Marine Forecasts

Abstract

Ostreopsis cf. ovata, a benthic toxic marine dinoflagellate, has been recorded along Italian coasts since the '90, but large bloom events have been reported only in recent years. In 2005, a monitoring programme started along the Ligurian coast (North-western Mediterranean), where time series of cell abundances have been collected for several sites, together with a range of related environmental variables. Data of cell abundances in 15 sites, together with environmental data provided by meteo-marine forecasting models used by the Regional Environmental Agency (ARPAL), have been used to implement a predictive modelling tool, able to forecast Ostreopsis cells concentration threshold exceedance as a function of meteo-marine forecasts. Starting from the experience of the predictive model implemented in 2015, the Quantile Regression Forest (QRF) has been applied: the model has been trained on past data (from 2015 until 2017) and tested with data taken during the two last available years (2018 and 2019). The use of this extremely adaptable regression model to evaluate threshold exceedance has shown a good capacity to predict overcoming events at a given spatial location. This tool can help the Regional Agency in the decision making process, providing an alert when/where a given alarm threshold is exceeded in order to trigger the emergency procedures. This is a first step in defining a predictive sampling strategy able to better capture bloom events.Keywords: classification, regression, machine learning, environmental data, Ostreopsis