Yilun Liu
University of Wisconsin-Madison
12 Papers
Yilun Liu is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Computer science & Metaheuristic. The author has an hindex of 9, co-authored 11 publications. Previous affiliations of Yilun Liu include Sun Yat-sen University & South China Agricultural University.
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Papers
A multi-type ant colony optimization MACO method for optimal land use allocation in large areas
TL;DR: Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods and has an improvement of the total utility value over SA and GA methods.
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Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning
TL;DR: The use of online rental listings as a new reliable data source for mapping housing rent in Guangzhou, China is proposed and can provide useful hints for housing rent mapping in other geographical areas.
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Land-use decision support in brownfield redevelopment for urban renewal based on crowdsourced data and a presence-and-background learning (PBL) method
TL;DR: A method for integrating crowdsourced datasets and traditional datasets to measure dynamic information of land parcels and applies a presence and background data machine learning (PBL) model to assess the redevelopment suitability in mass is proposed.
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An integrated approach of remote sensing, GIS and swarm intelligence for zoning protected ecological areas
TL;DR: An integrated approach of remote sensing, GIS and modified ant colony optimization (ACO) is proposed for application in zoning protected ecological areas and demonstrates that the proposed method performs better than other methods, including simulated annealing, genetic algorithm, iterative relaxation, basic ACO, and density slicing.
Early warning of illegal development for protected areas by integrating cellular automata with neural networks
TL;DR: It is found that the fast urban development has caused significant threats to natural-area protection in the study area and the integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms.
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