Smart Gas Monitoring And Prediction System Using IOT And ML

Abstract: The dangerous effects of various forms of air pollution - as well as the presence of carbon monoxide (CO), ammonia (NH3), carbon dioxide (CO2), and other hazardous gases - pose a significant threat to both human health and the safety of our environment, especially in indoor spaces and in industrial settings. Because there are very few continuous monitoring systems, there is often a delay in detecting hazardous gaseous conditions, which increases the likelihood of accidents and negatively impacts health.This research project describes the design of a smart gas monitoring and prediction system that combines the Internet of Things (IoT) with Machine Learning (ML) to allow for the real-time monitoring and prediction of the concentrations of hazardous gases. This system is constructed using an ESP32 microcontroller, integrated with MQ-7, MQ-137, and SCD40 sensors to monitor CO, NH3, CO2, temperature, and humidity. All sensor data is captured and streamed to a ThingSpeak cloud service for storage, analysis, and visualization. To perform predictive analysis, a hybrid deep learning model consisting of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks is constructed to analyze the time series data and forecast future levels of hazardous gases. This hybrid deep learning model is intended to enhance the accuracy of hazard detection and provide an earlier indication of dangerous conditions. A web-based dashboard has also been developed, which provides both the real-time and predicted values of the sensor measurements. Ultimately, the smart gas monitoring and prediction system will provide continuous monitoring of gaseous conditions, provide notifications of any hazardous conditions in a timely manner, and provide the opportunity to take action by implementing proactive safety measures; thereby improving the overall quality of environmental monitoring and increasing safety for the public.

Keywords: Internet of Things (IoT), Smart Gas Monitoring, Machine Learning (ML), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), ESP32, MQ-7 Sensor, MQ-137 Sensor, SCD40 Sensor, Carbon Monoxide (CO), Ammonia (NH₃), Carbon Dioxide (CO₂), ThingSpeak, Air Quality Monitoring, Predictive Analytics