## 3 Results
### Comparison with LUCC Mainstream Model  
In the main urban area of Chongqing, the model proposed in this paper is used to simulate the multi-class changes with the current mainstream PLUS model [14] and FLUS model [36], and the three simulation results are compared (Table 6). The FLUS model is an ANN-CA multi-land use/cover simulation model based on the pattern analysis strategy (PAS) and unit conversion mechanism proposed in 2017. The system dynamics model and the CA model are deeply coupled to gradually simulate future land use/cover patterns. The PLUS model is an RF-CA multi-LUCC simulation model based on the land expansion analysis strategy (LEAS) and patch growth strategy proposed in 2020. Combined with random seed generation and threshold decline mechanism, the automatic generation of simulated patches in time and space dynamics explores the landscape layout of land use/cover patterns under different scenarios. PLUS model and FLUS model have been widely used since they were proposed, and have achieved good feedback.  
To ensure the reference and credibility of the experimental results, this model uses the same data as the other two models and optimizes the parameters of the PLUS model and the FLUS model multiple times to achieve the best simulation results. The experimental results are shown in Table 6. In several different metrics, the proposed model has better performance than PLUS model and FLUS model. Compared with the PLUS model, the FOM of the proposed model in this paper are 14.38% and 37.55% higher for 2015-2020 and 2010-2020. Compared with the FLUS model, the FOM were 14.93% and 37.74% higher, respectively.  
The PLUS model and FLUS model dynamic iteration module both use all land types to build a roulette wheel to achieve multi-land competition and use the roulette algorithm to determine the land status of the next iteration. Multi-land competition depends on the comprehensive conversion probability, and the change suitability is the most important part of the comprehensive conversion probability. When the intelligent algorithm is used to extract change suitability, the overall probability of land types with more samples is higher than that of land types with fewer samples, which leads to the overall decline of competitiveness of land types with fewer samples. PLUS model and FLUS model use roulette to increase the conversion opportunities of land types with low comprehensive conversion probability, which cannot solve the problem fundamentally. The RF-CNN-LSTM-CA model based on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Type** & **Proposed** & **FLUS** & **PLUS** \\ \hline \multirow{4}{*}{2015–2020} & FOM & 0.1744 & 0.0251 & 0.0306 \\  & Kappa & 0.9684 & 0.9629 & 0.9303 \\  & Accuracy & 0.9829 & 0.9641 & 0.9696 \\  & F1 & 0.9641 & 0.9493 & 0.8253 \\  & Hamming\_loss & 0.0171 & 0.0358 & 0.0303 \\ \hline \multirow{4}{*}{2010–2020} & FOM & 0.4662 & 0.0888 & 0.0907 \\  & Kappa & 0.9599 & 0.8923 & 0.8981 \\ \cline{1-1}  & Accuracy & 0.9785 & 0.9424 & 0.9449 \\ \cline{1-1}  & F1 & 0.8113 & 0.7769 & 0.7910 \\ \cline{1-1}  & Hamming\_loss & 0.0214 & 0.0576 & 0.0551 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The proposed model compared to PLUS model and FLUS model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Proposed** & **Degraded II** \\ \hline Time & 81 s & 1526 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of running time of dynamic iterative modules between the proposed model and the Degraded I model.  
the HQSSIA proposed in this paper uses the method of successively converting each type of land cell to avoid competition between most land types and reduce the probability of decision-making errors in land type competition.