Advanced Carbon Footprint Prediction Using Hybrid Machine Learning and Ai-Assisted Recommendations

Abstract

Rising levels of carbon emissions have emerged as a key factor to climate change requiring smart mechanisms of monitoring and mitigation. In this paper, CarbonIQ, a machine learning-based, generative AI-based, and IoT-based data collection integrated carbon footprint prediction and recommendation system will be introduced. The system uses user activity logs and real-time sensor values received by an ESP32 which has been pre-configured with the sensor tags. Gassed to the carbon emission estimation and prediction is connected to gas sensors. The proposed approach uses the Random Forest regression model with a hybrid sensor-fusion mechanism. Besides prediction, a generative AI module allows giving personalized carbon reduction recommendations based on users' actions and their past actions. This system has also been optimized with ranking-based feedback, which will increase the engagement with the system and encourage users to move forward. sustainable practices. That the proposed strategy is useful can be validated with experimental results that demonstrate important outcomes in emissions prediction and taking actionable steps. Incorporating prediction and recommendation into a single structure, one would be able to make better decision. and develop more data- driven solutions to sustainability.

Rising levels of carbon emissions have emerged as a key factor to climate change requiring smart mechanisms of monitoring and mitigation. In this paper, CarbonIQ, a machine learning-based, generative AI-based, and IoT-based data collection integrated carbon footprint prediction and recommendation system will be introduced. The system uses user activity logs and real-time sensor values received by an ESP32 which has been pre-configured with the sensor tags. Gassed to the carbon emission estimation and prediction is connected to gas sensors. The proposed approach uses the Random Forest regression model with a hybrid sensor-fusion mechanism. Besides prediction, a generative AI module allows giving personalized carbon reduction recommendations based on users' actions and their past actions. This system has also been optimized with ranking-based feedback, which will increase the engagement with the system and encourage users to move forward. sustainable practices. That the proposed strategy is useful can be validated with experimental results that demonstrate important outcomes in emissions prediction and taking actionable steps. Incorporating prediction and recommendation into a single structure, one would be able to make better decision. and develop more data- driven solutions to sustainability.