A review of machine learning applications in life cycle assessment studies
Abstract Life Cycle Assessment (LCA) is a foundational method for quantitative assessment
of sustainability. Increasing data availability and rapid development of machine learning …
of sustainability. Increasing data availability and rapid development of machine learning …
Modelling for digital twins—potential role of surrogate models
The application of white box models in digital twins is often hindered by missing knowledge,
uncertain information and computational difficulties. Our aim was to overview the difficulties …
uncertain information and computational difficulties. Our aim was to overview the difficulties …
[HTML][HTML] Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data
Feature selection wrapper methods are powerful mechanisms for reducing the complexity of
prediction models while preserving and even improving their precision. Meta-heuristic …
prediction models while preserving and even improving their precision. Meta-heuristic …
[HTML][HTML] The current and future uses of machine learning in ecosystem service research
Abstract Machine learning (ML) expands traditional data analysis and presents a range of
opportunities in ecosystem service (ES) research, offering rapid processing of 'big data'and …
opportunities in ecosystem service (ES) research, offering rapid processing of 'big data'and …
Surrogate-assisted and filter-based multiobjective evolutionary feature selection for deep learning
Feature selection (FS) for deep learning prediction models is a difficult topic for researchers
to tackle. Most of the approaches proposed in the literature consist of embedded methods …
to tackle. Most of the approaches proposed in the literature consist of embedded methods …
[HTML][HTML] How artificial intelligence uses to achieve the agriculture sustainability: Systematic review
V Sachithra, L Subhashini - Artificial Intelligence in Agriculture, 2023 - Elsevier
The generation of food production that meets the rising demand for food and ecosystem
security is a big challenge. With the development of Artificial Intelligence (AI) models, there …
security is a big challenge. With the development of Artificial Intelligence (AI) models, there …
Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty
This paper proposes an integrated stochastic mixed-integer linear programming model for
biofuel supply chain and landscape design optimization that considers the interactions …
biofuel supply chain and landscape design optimization that considers the interactions …
Projecting life-cycle environmental impacts of corn production in the US Midwest under future climate scenarios using a machine learning approach
Climate change is exacerbating environmental pollution from crop production. Spatially and
temporally explicit estimates of life-cycle environmental impacts are therefore needed for …
temporally explicit estimates of life-cycle environmental impacts are therefore needed for …
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 …
Efficient agricultural yield prediction using metaheuristic optimized artificial neural network using Hadoop framework.
CP Saranya, N Nagarajan - Soft Computing-A Fusion of …, 2020 - search.ebscohost.com
The low-resolution imagery of satellite is used extensively for monitoring crops and
forecasting of yield which has a major role to play in the operational systems. A combination …
forecasting of yield which has a major role to play in the operational systems. A combination …