THERMODYNAMIC ANALYSIS AND SHORT-TERM MICROCLIMATE FORECASTING USING AN IOT-BASED WEATHER MONITORING SYSTEM

Abstract

Accurate monitoring of atmospheric thermodynamic parameters remains a significant challenge in tropical environments, where rapid microclimatic transitions
often occur with limited localized forecasting systems. The absence of affordable,real-time monitoring tools capable of reliably predicting short-term weather variations necessitates the development of smart and adaptive solutions. This study therefore evaluates a smart IoT-based weather monitoring system for real-time measurement and short-term forecasting of key atmospheric parameters. The system integrates temperature and humidity sensors, a microcontroller-based data acquisition unit, and an embedded algorithm for computing dew point and heat index values. Data were recorded on November 12, 2025, between 14:30 and 15:15 at 5–10 second intervals to assess system responsiveness and forecasting capability.Observed temperatures ranged from 31.5 °C to 32.8 °C (mean 32.3 °C), relative humidity from 60% to 87.9% (mean 72.4%), dew point between 23.0 °C and 24.6°C, and heat index values from 42.0 °C to 52.92 °C (mean 47.8 °C). The system effectively detected rapid humidity fluctuations, tracked dew point variations indicative of atmospheric moisture dynamics, and highlighted heat–humidity interactions associated with extreme thermal stress conditions. Forecast outputs,including “Cloudy” and “Rain Likely,” closely corresponded with thermodynamic indicators, achieving predictive accuracy between 85% and 92%. The results demonstrate that the developed system is suitable for microclimate monitoring,precision agriculture, and early-warning applications in tropical regions. Future improvements will focus on integrating higher-accuracy sensors (±1% RH, ±0.5hPa), moisture-protected enclosures, and machine-learning-based forecasting algorithms to enhance long-term reliability and predictive performance.