# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 5 Conclusions  
* All of the selected driving factors have good explanatory power for LU types, and the ROC values for each class are greater than 0.8. The farmland in the study area is mainly affected by POP and DEM and shows a negative correlation with both. The forestland is mainly influenced by Town_d and River_d, while the grassland is mainly affected by DEM and MAP. The water area is most affected by DEM, and the construction land is mainly influenced by social and economic factors such as POP and GDP.
* The kappa coefficients of the CLUE-S model and PLUS model simulation results were 0.727 and 0.759, and their FOM values were 0.109 and 0.201. The PLUS model has better accuracy than the CLUE-S model, especially in simulating the two LU types of forestland and water in the study area. Moreover, the distribution of spatial location and area of each LU type in the PLUS simulation result map and the status map is more similar.
* Among the four scenarios, the area of construction land decreases with the increase in the area of farmland and forestland under scenario FEP. This shows that curbing the over-expansion of construction land and controlling the total areas of construction land is conducive to the protection of farmland and forestland, and safeguarding food security and ecological safety. Urban sustainability is stronger compared to scenario NG.  
**Author Contributions:** Formal analysis, investigation, writing--original draft preparation, and visualization, Z.Y.; methodology, funding acquisition, and writing--review and editing, M.Z.; Conceptualization and data curation, Z.Y., M.Z. and S.W.; software, Y.G.; supervision and project administration, T.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Anhui Province, China (grant number 2208085 MD88); the National Natural Science Foundation of China (grant number 41501226); and Research Fund for Doctoral Program of Anhui University of Science and Technology (grant number ZY020).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge the team of High-Performance Spatial Computing Intelligence Laboratory (HPSCIL) at China University of Geosciences (Wuhan) for developing the PLUS model, which can be downloaded at [[https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model](https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model)]([https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model](https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model)) (accessed on 1 June 2022); the Chinese Academy of Resource and Environment Sciences and Data Center ([[https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)]([https://www.resdc.cn/Default.aspx](https://www.resdc.cn/Default.aspx)), accessed on 11 June 2022); and the China National Earth System Science Data Center ([[http://nnu.geodata.cn:8008](http://nnu.geodata.cn:8008)]([http://nnu.geodata.cn:8008](http://nnu.geodata.cn:8008)), accessed on 10 June 2022) for providing data support.  
**Conflicts of Interest:** The authors declare no conflict of interest.