Applications of Deep Learning in Ecotoxicology: Predicting Chemical Impacts on Biodiversity

Abstract

This paper looks into the potential of deep learning algorithms applied to predict the effects of chemical pollutants on biodiversity, especially in aquatic ecosystems. “Machine learning models explored were Random Forest, Support Vector Machine, Neural Networks, and Gradient Boosting.” The environmental data analysis used was applied to four machine learning models to predict chemical toxicity in water bodies. The concentration levels of chemicals along with biodiversity indices from various ecological studies and environmental monitoring sources were used. The models mentioned above were trained as well as tested upon their predictability accuracy in ecological results, such as the health of species and water quality. The following results were yielded: 92% with Random Forest, close with Neural Networks at 88%, then Gradient Boosting at 85%, and lastly Support Vector Machine at 80%. The performances of the models have been compared in terms of F1-score, precision, and recall, among others. In all, Random Forest has managed to score the maximum balance. This study shows that deep learning may potentially help make predictions about the effects of pollutants at least better than the contemporary comparative models. Overall, the results can really power new contributions to biodiversity conservation based on almost sure, data-driven insights into the ecological impacts of chemical pollutants..