Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas

Abstract

Jellyfish blooms along coastal areas can pose significant challenges for beach users and local authorities. Understanding the factors influencing jellyfish presence is crucial for effective management and mitigation strategies. In this study, citizen science data from the Andalusian coast (232 beaches, in 40 different localities) and machine learning techniques are used to investigate if the presence and absence of jellyfish along coastal areas can be predicted. A multi-layer perceptron (MLP) neural network was employed to classify user comments regarding jellyfish presence or absence, achieving an accuracy of approximately 96%. The MLP model demonstrated robustness in handling non-linear classification problems and noise, although it showed lower precision for predicting jellyfish presence, likely due to an imbalance in the dataset. Environmental data were also incorporated to characterise the influence of sea surface temperature, wind direction and wind speed on jellyfish distribution. The results align with previous studies, suggesting these environmental factors significantly impact jellyfish presence. Synthesis and applications. This research provides actionable recommendations for beach management. The implementation of continuous monitoring of sea surface temperature and wind conditions will enable more accurate predictions of jellyfish distribution. Adaptive management strategies that respond dynamically to environmental data will help mitigate the impact of jellyfish blooms on coastal tourism and public health