Abstract
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.