IoT-driven real-time weather measurement and forecasting mobile application with machine learning integration

The importance of accurate and timely weather information cannot be overstated, as it is crucial for daily activities, safety, and decision-making across various sectors. Existing weather forecasting systems often lack the precision required for localized conditions, relying on data from distant weather stations and limited environmental parameters. This paper introduces a real-time weather forecasting mobile application that integrates machine learning and IoT technology to address these challenges effectively. The system incorporates a mobile application designed to provide users with real-time weather updates through an intuitive and easy-to-use platform. It utilizes IoT sensors to collect comprehensive environmental data, including temperature, humidity, wind speed, barometric pressure, and rainfall, which are strategically deployed to ensure the collection of localized, high-resolution weather data in real-time. Additionally, the system leverages LoRa technology for robust long-range data transmission. It employs an Incremental Learning model that continuously adapts to new environmental inputs, thereby enhancing forecasting precision and efficiency. APIs (Application Programming Interface) enable efficient data input and retrieval, guaranteeing smooth connection and integration between the sensors and the forecasting algorithms. Moreover, we analyze forecasts from Google and systematically compare them with our localized predictions to highlight the advantages of site-specific deployment for achieving superior localized outcomes. This creative method offers a scalable and flexible solution that can be expanded to cover larger geographic areas in addition to providing precise weather forecasts. The project addresses the limitations of existing weather applications by delivering precise local weather conditions and an intuitive user experience. The initial implementation in Gazipur, Bangladesh, demonstrates the system’s effectiveness and potential for wider application nationwide.