## 6 Conclusions  
Based on the experimental data of multiple sets of indoor rainfall landslide models and combined with computer vision technology, the fault areas of each experimental slope were quantitatively identified, and the evolution of the deformation area of the landslide under different experimental conditions was studied. We also propose an S-curve model to predict landslide volume and effectively verified it through six physical experiments. These valuable conclusions include:  
1. Under continuous rainfall conditions, a certain amount of rainwater will first converge at the foot of the slope, causing the first damage to occur at the foot of the slope and then triggering larger-scale damage. In this process, the slope damage degree shows the trend of the S-growth curve, and the slope damage mode also shows two types of damage, namely, nudge damage and progressive backward push.
2. The intensity of rainfall and landslide volume are negatively correlated. The larger the slope angle is, the faster the slope destabilization damage is, but the landslide volume value is the largest for the slope angle of 45\({}^{\circ}\). The presence of a slope roof platform will accelerate the damage of the slope and increase the degree of damage. The soil compaction degree of the base layer is higher, and a slope without confining pressure will have a greater degree of damage. In the absence of wind, vegetation plays a certain protective role in the stability of the slope, and the greater the vegetation cover density, the more obvious the protective effect. In the case of wind, the vegetation on the damage of the slope plays a certain role in enhancing it, and the greater the wind speed, the more serious the slope damage.
3. When a slope is washed by raindrops, and the surface runoff is formed by rainwater, the unstable particles on the surface of the slope will be carried away and then the direction of depth develops, as shown in the quantitative analysis curve of the deformation area above. The landslide volume change has a certain time lag relative to the area change, and the landslide volume continues to increase after the landslide area tends to be stable.
4. Compared with the time series analysis moving-average model for predicting landslide volume change, the improved S-shaped growth curve model with two landslide volumes at 5 min intervals and influencing factor (i) as an input can predict the change process of landslide damage degree more accurately.  
Data organization, statistical analysis, writing--original draft, Y.L.; software, investigation, supervision, Q.L.; conceptualization, methodology, Y.Z.; software, investigation, C.Y.; validation, formal analysis, B.D.; validation, Q.K.; funding acquisition, methodology, writing--review and editing, W.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Jiangxi Provincial Natural Science Foundation (No. 20212 ACB214005) and the Major Science and Technology Projects of Anhui Province (No. 202003a0702002).  
Not applicable.  
**Data Availability Statement:** Please contact the corresponding author when you need the data on the results of this study.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.