ROLE OF DEEP LEARNING IN PREDICTION OF ENVIRONMENTAL CONDITIONS

Abstract

Accurate prediction of environmental conditions is fundamental for climate resilience, sustainable resource management, disaster mitigation, and public health planning. Traditional statistical and shallow machine learning approaches often fail to adequately capture the nonlinear, high-dimensional, and spatio-temporal dependencies inherent in environmental systems. Deep learning has emerged as a transformative computational paradigm capable of hierarchical feature learning and complex pattern extraction (Bengio, 2009; LeCun, Bengio, & Hinton, 2015). This study presents a comprehensive and critically synthesized review of deep learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and hybrid architectures for environmental prediction applications. Detailed analysis of methodological frameworks, data preprocessing strategies, model architectures, validation procedures, and performance metrics is provided. Comparative evaluation demonstrates that deep learning approaches consistently outperform traditional regression and support vector techniques in rainfall forecasting, air quality estimation, hydrological modeling, and climate trend analysis (Smola & Scholkopf, 2004; Zhang, 2003). Despite challenges related to interpretability and computational complexity, deep learning offers substantial potential for advancing environmental sustainability research.