# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 4 Discussion  
This study analyzed the historical LUCs in the study area and explored the evolution process. Then, a model with high accuracy was selected to simulate future LUs. The study results can be used for regional LU planning to provide some decision support. The transformation between LU types was calculated in the study, with the conversion of farmland into construction land as the main focus; this finding is the same as that of the previous research on Huaibei City [36]. The study area showed a continuous increase in the area of construction land and a decrease in the total area of farmland; the results \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Scenario Analysis** & **Farmland** & **Forestland** & **Grassland** & **Water** & **Construction Land** \\ \hline
2020 & & 42,258.87 & 1220.13 & 8425.98 & 3208.59 & 17,538.66 \\  & NG & 40,575.06 & 918.63 & 9126.72 & 4130.46 & 17,901.36 \\  & FP & 44,128.44 & 817.47 & 8391.06 & 3525.93 & 15,789.33 \\
2025 & EP & 41,801.85 & 1306.35 & 8981.10 & 4181.40 & 16,381.53 \\  & FEP & 43,986.78 & 1306.35 & 8245.44 & 3576.96 & 15,356.70 \\  & NG & \(-\)1683.81 & \(-\)301.50 & 700.74 & 921.870 & 362.70 \\  & FP & 1869.57 & \(-\)402.66 & \(-\)34.92 & 317.34 & \(-\)1749.33 \\
2020–2025 & EP & \(-\)457.02 & 86.22 & 555.12 & 972.81 & \(-\)1157.13 \\  & FEP & 1727.91 & 86.22 & \(-\)180.54 & 368.37 & \(-\)2001.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Changes of the regional area under different scenarios in 2020–2025 (hm\({}^{2}\)). of this study are the same as the findings of [PERSON] et al., in that farmland shows a gradual decrease trend and construction land shows a gradual increase trend [37]. Among the driving factors with strong explanatory power for farmland are POP, DEM, MAP, GDP, Town_d, etc., which is similar to the results of previous studies [38]. The driving factors with stronger explanatory power for construction land are mainly the POP, GDP, MAP, DEM, etc., which are dominated by socioeconomic factors, which is similar to the findings of [PERSON] et al. [21]. Forestland and grassland are mainly influenced by natural driving factors, such as River_d, DEM, MAP, etc. The driving factor with the strongest explanatory power for water was DEM; similar conclusions were reached by previous authors, and the results of [PERSON] et al., showed that slop was the main expansion factor affecting grassland, followed by MAP, and the highest driving factors affecting the expansion of water were elevation, population density, and distance to the water system [39]. Both the improved CLUE-S model and the PLUS model have their advantages. The improved CLUE-S model incorporates spatial autocorrelation factors, which can enhance the overall accuracy of the model simulation [40]. In contrast, the newly developed PLUS model, can explore potential driving factors and better simulate LUCs at the patch level to obtain better simulation accuracy. The comparison of the simulation results of CLUE-S and PLUS models in the study area revealed that the PLUS model outperforms the improved CLUE-S model in terms of spatial location distribution and quantity at the same scale resolution, especially in the simulation of ecological LU types. Moreover, the accuracy of the PLUS model is better at fine resolution, while the CLUE-S model is not. The CLUE-S model has similar accuracy to PLUS in simulating construction land and farmland, but the area of simulation results will be greater than in the PLUS model. Similar conclusions have been drawn from earlier studies. [PERSON] et al., showed that the PLUS model outperformed the CLUE-S model in spatial location distribution and quantity prediction, and the CLUE-S model had higher simulation accuracy in flat terrain areas [41]. [PERSON] et al., set up comprehensive scenarios based on different policy requirements as well as economic development and environmental protection needs [6]. [PERSON] et al., set up scenarios SSP126, SSP245, and SSP585 based on the SSP-RCP scenarios provided by CMIP6.