Environmental Science

Environmental science increasingly leverages machine learning (ML) and artificial intelligence (AI) to address complex challenges, focusing on improved data analysis and predictive modeling. Current research emphasizes the application of techniques like recurrent neural networks (RNNs), geospatial modeling with ML, and topological data analysis (TDA) for tasks such as ecosystem monitoring, climate change prediction, and resource management. This integration of AI and ML is crucial for enhancing the accuracy, efficiency, and interpretability of environmental research, ultimately informing better policy decisions and resource management strategies while simultaneously raising important ethical considerations regarding bias and accountability.