Environmental sustainability has become a global priority as rapid industrialization, climate change, and resource depletion continue to intensify ecological challenges. Managing environmental systems requires the ability to process massive volumes of real-time data collected from diverse natural and human-driven activities. This research explores the application of Big Data Analytics for enhancing environmental sustainability by examining large-scale ecological signals captured through modern digital technologies. These signals include satellite imagery, IoT- enabled air and water quality sensors, smart energy meters, climate monitoring systems, agricultural field sensors, and geospatial data from remote sensing platforms. By integrating these heterogeneous environmental data streams, the study introduces an intelligent analytical framework capable of detecting ecological changes, predicting pollution levels, forecasting climate variations, and optimizing resource usage. Using machine learning and deep learning algorithms, the framework processes complex environmental datasets to generate highly accurate predictive insights. The results show that Big Data-based environmental analysis significantly enhances sustainability planning, improves disaster preparedness, supports conservation strategies, and strengthens decision-making for climate-resilient smart cities.
Gomathy et al. (Sat,) studied this question.