Smart Weather Station for Real-Time Climate Monitoring and Prediction Using IoT and Machine Learning Technologies

Abstract

Accurate hyper-local climate prediction is essential for decision-making in agriculture, urban planning, and environmental management. This study addresses the scarcity of accessible monitoring solutions by developing an autonomous, IoT-enabled smart weather station that integrates high-frequency data acquisition with real-time Deep Learning forecasting. The hardware architecture utilizes an Arduino™ for multi-sensor data collection—covering temperature, humidity, pressure, wind dynamics, and solar radiation—coupled with LoRa™ for robust wireless transmission. A Raspberry® Pi 3 functions as an edge computing platform for localized processing and inference. Predictive performance was optimized through a multivariate Long Short-Term Memory (LSTM) network designed to capture non-linear temporal dependencies. Operational validation confirmed high data integrity, comparable to high-end commercial stations. The LSTM model significantly outperformed traditional architectures, yielding a Coefficient of Determination (R2) of 0.957 and a Mean Squared Error (MSE) of 0.0434. This research provides a reliable, low-latency solution for localized forecasting by successfully synthesizing high-precision models for edge deployment. The scalable architecture represents a significant advancement toward actionable, data-driven decision-making in climate-sensitive sectors, offering a low-uncertainty alternative for sustainable resource management and smart initiatives in resource-constrained environments.