Big Data and Machine Learning for Climate Change Prediction: An Integrated Approach to Environmental Monitoring

The integration of big data and machine learning (ML) in climate change prediction offers a transformative approach to environmental monitoring, enabling improved accuracy, scalability, and actionable insights. This study presents an integrated framework that combines the strengths of big data technologies and advanced ML algorithms to address the complexities of climate systems. Diverse datasets from satellites, IoT sensors, and historical records were preprocessed using normalization, feature engineering, and dimensionality reduction techniques to ensure data quality and compatibility. ML models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, were developed to tackle spatial and temporal prediction tasks. The models demonstrated robust performance, with CNNs achieving 93.5% accuracy in deforestation detection and hybrid CNN-RNN models delivering 95% accuracy in cyclone trajectory prediction. Real-time monitoring applications, such as flood risk assessment and air quality forecasting, showcased low latency and scalability, reinforcing the framework's operational viability. Despite these advancements, challenges such as data bias, computational demands, and algorithmic transparency remain significant. To address these, solutions like dataset augmentation, edge computing, and explainable AI are proposed. This research highlights the potential of integrating big data and ML for dynamic and precise climate predictions while emphasizing the need for ethical and equitable deployment of these technologies. By fostering interdisciplinary collaboration and leveraging emerging computational innovations, this study contributes to advancing climate resilience and sustainable environmental management.