Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system’s advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak’s cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management.