MACHINE LEARNING-BASED FORECASTING OF BIOACCUMULATION AND HISTOPATHOLOGICAL EFFECTS IN AQUATIC ORGANISMS
Abstract
Heavy metal contamination in freshwater environments poses significant risks to aquatic organisms and human health, as these heavy metals enter freshwater systems through various sources, including industrial waste, agricultural runoff, mining and atmospheric deposition. Efforts to develop efficient methods for removing heavy metals from wastewater have gained momentum in recent years. This study focuses on machine learning (ML) models for predicting the bioaccumulation and histopathological effects of heavy metal pollutants on aquatic life under various climate change scenarios. The ML models have shown promise in forecasting the impacts of heavy metal pollution on freshwater ecosystems and informing conservation strategies. It is crucial to understand the complex interactions between environmental factors, climate change and ecosystem health. This study discusses the importance of incorporating diverse species and environmental factors in these models and acknowledges potential challenges, such as inaccuracies and data misinterpretation. Enhancing the predictive capabilities of ML models is essential for better environmental management and conservation practices via refinement and validation of models using updated data and advanced methodologies. This study also emphasizes the broad potential of ML in environmental research, improvement of model capabilities and challenges posed by heavy metal pollution and climate change.
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