Artificial Intelligence Prediction of Heavy Metal Exposure in Wetland Birds

Abstract

Wetlands play a crucial role in providing habitats for different types of birds and maintaining ecological balance. However, there has been a rise in heavy metal concentrations in wetlands due to the growing discharges from industries, agriculture, and urban activities, and these may lead to possible threats to the health of the birds. Traditional approaches in evaluating the levels of exposure to heavy metals rely on field work and laboratory procedures, which may take long periods and fail to accurately detect associations between the environment and biological indicators. In this research, a framework for predicting heavy metal exposure risks in wetlands birds using artificial intelligence is proposed through incorporating environmental and biological indicators. It incorporates attributes such as the characteristics of the wetlands, pollution attributes, bird attributes and heavy metal contamination parameters in order to establish the patterns of contamination and classify exposure risks. The performance analysis has shown that the developed AI framework has been able to predict the contamination levels accurately at 94% compared to other models such as Decision Tree (82%), Support Vector Machine (85%), Random Forest (88%) and Artificial Neural Network (91%). These results have shown that the use of AI for prediction can effectively facilitate the analysis of ecological interactions and help in identifying the high-risk wetland areas. This suggested method offers an excellent platform for wildlife researchers and conservation organizations to assess early on any risk of contamination and sustain wetlands management. Integration of the future application with real-time environmental sensors and long-term ecological data will increase the accuracy of artificial intelligence-based conservation efforts