Publication
Life cycle assessment using machine learning
| dc.contributor.advisor | Faria, Brígida Mónica | |
| dc.contributor.advisor | Oliveira, Alexandra Alves | |
| dc.contributor.advisor | Pinto, Edgar | |
| dc.contributor.author | Gomes, Sofia Carolina Moura | |
| dc.date.accessioned | 2025-02-13T11:59:17Z | |
| dc.date.available | 2025-02-13T11:59:17Z | |
| dc.date.issued | 2024-11-28 | |
| dc.date.submitted | 2024-11-28 | |
| dc.description.abstract | Life Cycle Assessment (LCA) is a scientific methodology that allows for assessing the impacto f a producto or servisse on the environment, throughout its life ccycle. It includes defining objectives and contexto, inventory, impact assessment, and interpretation phases. Artificial Itelligence (AI) refers to computer systems capable of performing tasks that typically require human intelligence. Machine Learning (ML) is na área of AI that envolves the development of algorithms capable of learning from data and making predictions or decisions based on data. LCA and ML have been combined o overcome LCA’s complexity at various stages and for different purposes, namely, to develop surrogate LCA tools. This study focuses on the application of ML in the Life Cycle Inventory (LCI) phase to find pollutant emissions generated into the environment to complete the LCI phase of the LCA. The presente work seeks to answer the following question: “Can Machine Learning techniques be applied to predict outcome variables of the LCI phase of LCA?”. These variables include all the inouts and outputs throughout the life cycle of a producto. The database used in this work comprises 865 observations containing agricultural input variables (e.g. chemical fertilizer, pesticides, huma labor, diesel fuel) and production output (yield and environmental emissions). The data was collected from literature and refers to kiwi, watermelon, citrus, tea, and hazelnut crops in Guilan province in northern Iran. Na expert in the field validated the estimation of pollutant emissions, calculated using Agri-footprint 4.0 and the updated version Agri-footprint 6. Additional key methodologies, standards and reports were also cponsulted for this research. Th Decision Tress and Neural Network models developed were able to estimate the pollutant emissions generated into the environment throughout the production process. The results of the Absolute Normalized Error for the Decision Tree, Neural Network1 and Neural Network2 were 1124.79, 0.07 and 0.14 respectively. The Friedman test, with p-value˂ 0.001, less than α=0.05, reveals statistically significant diferences in the Absolute Normalized Error values in at least one of the models. The Wilcoxon tes (p-value˂0.001)indicates significant diferences between all the models. | por |
| dc.identifier.tid | 203852460 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/29501 | |
| dc.language.iso | eng | |
| dc.rights.uri | N/A | |
| dc.subject | Life cycle assessment | |
| dc.subject | Sustainability | |
| dc.subject | Machine learning | |
| dc.subject | Decision trees | |
| dc.subject | Data analysis | |
| dc.title | Life cycle assessment using machine learning | por |
| dc.type | master thesis | |
| dspace.entity.type | Publication |
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