Machine Learning Prediction of Macroinvertebrate Responses to Water Quality Variations

Abstract

Introduction: The freshwater ecosystem hosts various aquatic organisms, where macroinvertebrates have a significant role in the ecological balance and determination of environmental health. The variation in water quality due to pollution, eutrophication, changes in habitats, and climatic factors has a direct impact on the distribution, abundance, and biodiversity of the macroinvertebrate community. Traditional ecological analysis methods usually rely on lengthy field analyses and may face challenges in predicting the biological response to changing environmental conditions. Hence, there is a need to predict the impact of variation in water quality on the response of aquatic animals. Materials & Methods: This research provides a machine learning-based ecological analysis method that will predict the macroinvertebrate response under different water quality conditions. Biological data such as abundance, diversity, and community analysis are combined with environmental data such as pH, temperature, Dissolved Oxygen, Turbidity, and Nutrient Content. This approach uses associations between changes in water quality and the behavior of macroinvertebrates to analyze the conditions of freshwater. Findings: This assessment illustrates that environmental condition changes affect the distribution and ecology of macroinvertebrates. Waters with good conditions have diverse and sensitive macroinvertebrates, while poor conditions favor pollution-resistant macroinvertebrates. Predictive analysis plays an instrumental role in highlighting the major environmental factors influencing aquatic biodiversity. Conclusion: This assessment underscores the significance of combining predictive methods with biological assessments in freshwater management. Machine-learning-based prediction is a valuable tool for predicting the impact of the environment on macroinvertebrates, which can be instrumental in the early detection of ecosystem deterioration and sustainable water management.