Advances in application of machine learning to life cycle assessment: a literature review
Abstract Purpose Life Cycle Assessment (LCA) is the process of systematically assessing
impacts when there is an interaction between the environment and human activity. Machine …
impacts when there is an interaction between the environment and human activity. Machine …
State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery
Abstract Machine learning (ML) has emerged as a significant tool in the field of biorefinery,
offering the capability to analyze and predict complex processes with efficiency. This article …
offering the capability to analyze and predict complex processes with efficiency. This article …
Life cycle assessment of food loss and waste in the food supply chain
Y Omolayo, BJ Feingold, RA Neff… - Resources, Conservation …, 2021 - Elsevier
Addressing food loss and waste (FLW) globally is critical for both improving food security
and mitigating environmental pollution. While there are numerous studies addressing FLW …
and mitigating environmental pollution. While there are numerous studies addressing FLW …
A review of inventory modeling methods for missing data in life cycle assessment
Missing data is the key challenge facing life cycle inventory (LCI) modeling. The collection of
missing data can be cost‐prohibitive and infeasible in many circumstances. Major strategies …
missing data can be cost‐prohibitive and infeasible in many circumstances. Major strategies …
Modelling carbon dioxide emissions under a maize-soy rotation using machine learning
Climatic parameters influence CO 2 emissions and the complexity of the relationship is not
fully captured in biophysical models. Machine learning (ML) is now being applied to …
fully captured in biophysical models. Machine learning (ML) is now being applied to …
[HTML][HTML] Automatic modeling of socioeconomic drivers of energy consumption and pollution using Bayesian symbolic regression
Predicting countries' energy consumption and pollution levels precisely from socioeconomic
drivers will be essential to support sustainable policy-making in an effective manner. Current …
drivers will be essential to support sustainable policy-making in an effective manner. Current …
Sustainable systems engineering using life cycle assessment: application of artificial intelligence for predicting agro-environmental footprint
F Mohammadi Kashka, Z Tahmasebi Sarvestani… - Sustainability, 2023 - mdpi.com
The increase in population has increased the need for agricultural and food products, and
thus agricultural production should be increased. This goal may cause increases in …
thus agricultural production should be increased. This goal may cause increases in …
Review of machine learning and deep learning models in agriculture
Machine learning (ML) refers to the processes that enable computers to think based on
various learning methods. It can be also called domain which is a subset of Artificial …
various learning methods. It can be also called domain which is a subset of Artificial …
Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects
G Larrea‐Gallegos… - Journal of Industrial …, 2022 - Wiley Online Library
Land use changes (LUCs), which are defined as the modification in the use of land due to
anthropogenic activities, are important sources of GHG emissions. In this context …
anthropogenic activities, are important sources of GHG emissions. In this context …
Application of classic and soft computing for modeling yield and environmental final impact in vegetable production (a case study: transplanting onion in Isfahan …
This study aimed to develop a precision model between inputs and yield, and also between
inputs (indirect emissions) and environmental final index (EFI) in onion farms through …
inputs (indirect emissions) and environmental final index (EFI) in onion farms through …