Gravity-Informed Deep Learning Framework for Predicting Ship Traffic Flow and Invasion Risk of Non-Indigenous Species via Ballast Water Discharge
CoRR(2024)
摘要
Invasive species in water bodies pose a major threat to the environment and
biodiversity globally. Due to increased transportation and trade, non-native
species have been introduced to new environments, causing damage to ecosystems
and leading to economic losses in agriculture, forestry, and fisheries.
Therefore, there is a pressing need for risk assessment and management
techniques to mitigate the impact of these invasions. This study aims to
develop a new physics-inspired model to forecast maritime shipping traffic and
thus inform risk assessment of invasive species spread through global
transportation networks. Inspired by the gravity model for international
trades, our model considers various factors that influence the likelihood and
impact of vessel activities, such as shipping flux density, distance between
ports, trade flow, and centrality measures of transportation hubs.
Additionally, by analyzing the risk network of invasive species, we provide a
comprehensive framework for assessing the invasion threat level given a pair of
origin and destination. Accordingly, this paper introduces transformers to
gravity models to rebuild the short- and long-term dependencies that make the
risk analysis feasible. Thus, we introduce a physics-inspired framework that
achieves an 89
trajectories and an 84.8
key port areas, representing more than 10
deep-gravity model. Along these lines, this research contributes to a better
understanding of invasive species risk assessment. It allows policymakers,
conservationists, and stakeholders to prioritize management actions by
identifying high-risk invasion pathways. Besides, our model is versatile and
can include new data sources, making it suitable for assessing species invasion
risks in a changing global landscape.
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