[en] Since the Stockholm Declaration on the human environment in 1972, there has been a growing recognition of the impact of human activities on Earth's ecosystems. This has created an increasing need for modeling and predicting the resilience of ecosystems, which is crucial not only for understanding ecosystem patterns and processes but also for addressing climate change and implementing effective conservation and management strategies. Despite the importance of this issue, the intrinsic complexity of ecosystems and the lack of sufficient data present considerable challenges. To address these challenges, we propose an approach that combines model-driven engineering and artificial intelligence. Specifically, we propose a formalization for modeling and verifying ecosystem requirements, a method for synthesizing heterogeneous ecosystem resilience data, and a product line of neural network architectures adaptable to diverse properties and types of ecosystem scenarios to study. Additionally, we propose a model-driven process specification detailing the different artifacts, stakeholder roles, tasks, and model transformations of the proposed approach. This paper outlines the problem and preliminary work, presents the proposed approach, which is the current focus of an ongoing Ph.D. thesis, and discusses the future research contributions.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
SOUSA, Tiago ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Towards Modeling and Predicting the Resilience of Ecosystems
Date de publication/diffusion :
2023
Nom de la manifestation :
ACM/IEEE 26th International Conference on Model-Driven Engineering Languages and Systems
Organisateur de la manifestation :
ACM SIGSOFT, IEEE TCSE
Lieu de la manifestation :
Västerås, Suède
Date de la manifestation :
01-10-2023 to 06-10-2023
Manifestation à portée :
International
Titre de l'ouvrage principal :
Towards Modeling and Predicting the Resilience of Ecosystems
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