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- W3143022262 abstract "In the field of urban dynamic simulation, scholars are committed to exploring different models and methods to improve simulation accuracy. However, while the overall accuracy is improved, the imbalance of data results in relatively low accuracy of minority samples. How to improve the simulation accuracy of minority samples has become an important issue. This paper will solve this problem at the sampling level. The experiment is based on the GlobeLand30 land use data in the three phases of 2000, 2010, and 2020, and uses the ProWSyn (Proximity Weighted Synthetic Oversampling Technique) algorithm to balance the data. Train the model with land use data from 2000 to 2010, verify the model with data from 2010 to 2020, and finally simulate the future based on data from 2020. The results show that the ProWSyn-MLP-CA model constructed in this paper has improved the accuracy of minority samples to varying degrees. In the case of using the same neural network model, the KAPPA value of a small number of land types increased by an average of 30% after ProWSyn equalization." @default.
- W3143022262 created "2021-04-13" @default.
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- W3143022262 date "2021-03-01" @default.
- W3143022262 modified "2023-10-16" @default.
- W3143022262 title "Urban land use simulation based on ProWSyn-MLP-CA" @default.
- W3143022262 cites W1973749534 @default.
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- W3143022262 doi "https://doi.org/10.1088/1755-1315/692/4/042020" @default.
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