首页|New Findings in Machine Learning Described from Chalmers University of Technolog y (Development of a machine learning model to improve estimates of material stoc k and embodied emissions of roads)
New Findings in Machine Learning Described from Chalmers University of Technolog y (Development of a machine learning model to improve estimates of material stoc k and embodied emissions of roads)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting originating from Gothenburg, Sweden, by NewsRx correspondents, research stated, "Material flow analysis is an important tool for estimating material flows and embedded emissions of transpor t infrastructure." Financial supporters for this research include Stiftelsen For Miljostrategisk Fo rskning. The news correspondents obtained a quote from the research from Chalmers Univers ity of Technology: "Missing attributes tend to be a major barrier to accurate es timates. In this study a machine learning model is developed to estimate the mis sing data in a statistics dataset of roads, to enable a bottomup material stock and flow analysis. The proposed approach was applied to the Swedish road networ k to predict missing data for road width in the statistical dataset. The predict ed hybrid dataset was then used to estimate material stocks, flows, and embodied emissions from Year 2020 to Year 2045 using decarbonization scenarios with a su pply chain perspective. The study demonstrates that machine learning models can be used to enable national-level material stock and flow analyses of roads. Mult iple machine learning algorithms were tested, and the best performing model achi eved an R2 value of 0.784."
Chalmers University of TechnologyGothe nburgSwedenEuropeCyborgsEmerging TechnologiesMachine Learning
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