Harnessing Machine Learning With Iot For Intelligent Environment Monitoring And Sustainable Ecosystem Management

Abstract

Machine learning, a prevailing application of Artificial Intelligence (AI) is an effective weapon in the fight against environmental degradation that can model climate change and develop more sustainable production techniques. Meanwhile, the Internet of Things (IoT) can transmit data across networks without the need for human intervention.  It is essential to identify dangerous materials in the environment in order to safeguard ecosystems and human health.  Artificial intelligence (AI), as technology develops, has become a promising technique for developing sensors that can efficiently identify and evaluate these dangerous materials. The use of information technology for environmental pollution detection is becoming more popular as a result of its rapid improvements.  The Internet of Things (IoT), machine learning (ML), and sensor systems can be effectively employed for environmental monitoring, including the detection of soil toxins, water pollutants, and air pollutants.  This paper describes the development of an Internet of Things (IoT)-based system that uses sensor networks to detect CO, CO2, and alcohol in a real-time environment.  To gather real-time data on air pollution, such systems have been erected at different locations across the city.  In order to identify the sustainable environment, the collected data was processed using an ML model on the central server. Additionally, results can be posted on the website for public distribution.