Web-Based Decision Support System for Remote Weather Radar Maintenance: Design and Implementation

Weather radars play a crucial role in monitoring atmospheric conditions, especially in regions prone to adverse weather phenomena and near critical infrastructure hubs like airports. Ensuring their continuous and reliable operation is essential for accurate weather forecasting and early warning systems. This paper introduces a novel web-based Decision Support System (DSS) designed to enhance the maintenance and operational efficiency of weather radar systems through the integration of Artificial Intelligence (AI) and data analytics. By employing Deep Learning (DL) algorithms for anomaly detection across vibration, acoustic, and visual data, the system aims to preemptively identify potential failures and optimize maintenance schedules. Our methodology encompasses the development of a scalable, Docker-supported infrastructure with Flask for Application Programming Interface (API) management, ensuring a user-friendly experience for remote weather radar maintenance tasks. Preliminary results demonstrate the system's effectiveness in reducing downtime and maintenance costs, while significantly improving operational reliability. This study contributes to the field by showcasing the potential of AI in enhancing the predictive maintenance and reliability of critical infrastructures like weather radar systems.