Paper Title
AIRSMART: INTELLIGENT IOT-BASED AIR QUALITY MONITORING AND FORECASTING FOR SMART CREATIVE INTEGRATING PREDICTIVE ENVIRONMENTAL MODELS

Abstract
Airsmart is an intelligent IoT-based air quality monitoring and forecasting system for creating an innovative, creative campus. It integrates predictive environmental models to transform air pollution and enable timely decision-making. The system utilizes various technologies, such as BME680 for sensing temperature, humidity, gas, and wind altitude, Sensirion SPS for particulate matter (PM1.0, 2.5, 4.0, and 10), Node MCU V1 ESP 8266 for wifi connection and data logger, and two 3.7voltz battery for a power source. These technologies have shown promising results in gathering accurate air quality data. On the other hand, the extra trees regressor outperforms the different models in predicting air quality. Its exceptional performance across multiple evaluation metrics, including R-squared (0.9782), Mean Absolute Error (MAE) (1.0253), Mean Squared Error (MSE) (8.3520), Root Mean Squared Error (RMSE) (2.5239), Root Mean Squared Logarithmic Error (RMSLE) (0.0707), and Mean Absolute Percentage Error (MAPE) (0.0374), solidifies its position as the optimal choice for implementation within the AirSmart system. Keyword - DHT22 Temperature and Humidity Sensor, SPS30 Particle Matter Sensor, ESP32 microcontroller, Extra Trees Regressor