IOT And Machine Learning Integration For Predictive Environmental Modelling

Abstract

The integration of Internet of Things (IoT) and machine learning (ML) techniques has gained significant attention in the field of environmental modelling, offering enhanced capabilities for real-time data acquisition, analysis, and prediction. This paper explores the application of IoT-based systems in conjunction with ML algorithms for predictive environmental modelling, with a focus on improving the accuracy and efficiency of environmental forecasts. The study utilizes various IoT sensors for data collection, including temperature, humidity, and air quality sensors, which provide continuous monitoring of environmental variables. Machine learning models, such as regression analysis, decision trees, and neural networks, are employed to analyze the collected data and generate predictive insights. The integration process is detailed, along with the challenges associated with data preprocessing, real-time processing, and system scalability. Results demonstrate that combining IoT with machine learning significantly improves the predictive accuracy compared to traditional methods. This approach has the potential to revolutionize environmental monitoring systems by enabling timely and data-driven decision-making for better sustainability management. The paper concludes with a discussion on future directions, including the application of edge computing and advanced AI techniques to further enhance real-time environmental prediction models.