TRANSFORMING ENVIRONMENTAL SCIENCE WITH DATA-DRIVEN MODELS: THE ROLE OF MACHINE LEARNING AND DEEP LEARNING

Abstract

The growing complexity and scale of environmental challenges, such as climate change, biodiversity loss, and pollution, necessitate a paradigm shift in analytical approaches. Traditional physical and statistical models often struggle with the non-linear, high-dimensional, and spatiotemporal nature of environmental data. This paper explores the transformative impact of data-driven models, specifically Machine Learning (ML) and Deep Learning (DL), in advancing environmental science. We provide a comprehensive review of their applications across key domains, including climate modeling, air and water quality forecasting, biodiversity monitoring, and precision conservation. A case study is presented, proposing a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for predicting PM2.5 air pollutant concentrations. Trained on a publicly available dataset of meteorological and pollutant data, the model demonstrates superior performance (MAE: 8.2 µg/m³, R²: 0.91) compared to traditional benchmarks. The results underscore the capability of DL models to capture intricate spatiotemporal dependencies. We discuss the challenges of data quality, model interpretability, and computational demands, concluding that the integration of ML/DL with domain knowledge is crucial for building robust, trustworthy, and actionable tools for environmental management and policy-making.