Using Artificial Intelligence to Predict Animal Behaviour in Food Webs
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Date
2017
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Transactions on Machine Learning and Artificial Intelligence
Abstract
Overfishing of species in the marine life has caused oceans to become deserts at a fast pace. The population of specific species such as Cod and Haddock has reduced over the years. This has affected countries that hugely depend on them as a source of food. This study used Dynamic Bayesian Network (DBN) to predict animal behaviour in a food web. Two independent biomass surveys from the North Sea were used to learn predictive models and test them on the Northern Gulf Ocean. The resulting predictive model is expected to unveil useful information about what affects the population of fishes in the Northern Gulf Ocean. In addition, the predictive model was used to make predictions into the future about the effects of tampering with the population of specific species of fish in the same region. The focus was on the Cod species in the George’s Bank in relationship to species network in their food web. Looking at their biomass states and the effects it has on the hidden dependence when there is a change in their biomass states. Also, the different predictive models were used to evaluate species in the George’s Bank based on their performance. The result from the experiment shows that there is a hidden dependence, which is responsible for the collapse of species (Cod); due to the temperature or salinity of the ocean.
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Citation
Aduragba, T., Ahmed, A., Jimada, B., Ajani, A., & Adedoyin, Y. (2017). Using Artificial Intelligence to Predict Animal Behaviour in Food Webs. Transactions on Machine Learning and Artificial Intelligence, 5(5), 98.