Empowering AI/ML with Local Rainfall Data: Sensor-Online’s IoT Platform Integrates LoRaWAN/NBIOT Sensors and SMHI Forecast🌧️
The ability to precisely monitor hyper-local weather conditions is essential as we adapt to more frequent climate variations. Through rainfall sensors connected via LoRaWAN, combined with the Sensor-Online IoT platform, we can capture critical metrics like:
*Local Rainfall Volume
*Rain Intensity
*Rainfall per Hour & Day
Sensor-Online captures real-time data and integrates current and forecasted weather insights from SMHI, Sweden’s expert meteorological and hydrological authority. SMHI enables us to pair real-time rainfall data with trusted, forecast-driven insights.
With these detailed inputs, Sensor-Online, collect, decode and presents data to powerful AI and machine learning (ML) models, transforming raw data into meaningful predictions. By capturing patterns and signals in both historical and real-time rainfall data, these models are essential for:
Enhanced Flood Prediction: AI models improve flood risk prediction by combining real-time rain data with SMHI forecasts, providing timely warnings.
Agriculture & Irrigation Optimization: Data-driven insights inform optimal watering schedules, conserving resources and boosting yields.
Urban Water Management: Accurate rain intensity measurements help cities design infrastructure to manage surges, reducing overflow risks and safeguarding urban areas.
By combining Sensor-Online’s high-precision rainfall data, SMHI’s authoritative forecasts, and AI’s predictive capabilities, we’re ushering in a new era of environmental monitoring that empowers communities to respond effectively to weather variability. 🌍
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