An Intelligent Framework for Environmental Impact Assessment Using Artificial Intelligence

Abstract

The restoration of terrestrial ecosystems promotes sustainable land resource development and aids in the preservation of the natural world. For increasingly severe land degradation, contemporary and effective strategies for the preservation of ecological purposes must be developed The proposed framework integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyze environmental data obtained from Environmental Product Declarations (EPDs) and Life Cycle Assessment (LCA) reports. NLP is employed to extract and process relevant environmental information, while a Random Forest algorithm is utilized to develop predictive models for environmental impact assessment. The framework is trained using product-specific data and seven environmental impact categories and subsequently validated using an independent testing dataset. Our findings show that the model had an accuracy of 85%, 72%, 65%, and 71% in predicting the values of the following impact categories: global warming possibility, abiotic depleting potential for fossil fuels, acidity capacity, and the photochemical ozone generation potential. Our approach shows that by learning from the outcomes of the earlier LCA research, sustainability can be predicted with a defined variability. The quantity of data provided for training also affects the model's efficacy.