International Journal of Science, Engineering and TechnologyAuthors: Gulam Jilani, Assistant Professor Ruchi Dronawat, Assistant Professor Rupali Chaure
Abstract: The rapid growth of smart technologies and Internet of Things (IoT) devices has enabled the collection of large-scale environmental and urban data. Predictive analytics powered by deep learning techniques has emerged as a powerful tool to transform this data into actionable insights. This paper presents an integrated deep learning-based predictive analytics framework designed for smart farming, flood and landslide prediction, and intelligent traffic management systems. The proposed approach leverages historical and real-time sensor data, satellite imagery, weather parameters, and traffic flow information to generate accurate forecasts and support decision-making processes.For smart farming, the system predicts crop yield, soil health conditions, and irrigation requirements using time-series models. In disaster management, the framework utilizes environmental and geospatial data to forecast flood risks and landslide occurrences, enabling early warning systems. In intelligent traffic systems, deep learning models analyze vehicle density, road conditions, and traffic patterns to optimize signal timing and reduce congestion. The proposed framework integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid architectures to capture spatial and temporal dependencies in complex datasets. The results demonstrate that deep learning-based predictive analytics significantly improves accuracy, efficiency, and responsiveness compared to traditional statistical approaches. This study highlights the potential of unified predictive systems in building sustainable smart environments.
