# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 6 Conclusions  
This study focussed on several typical areas in the southern Anhui mountainous region as the research area. Based on 11 characteristic factors, such as elevation and historical landslide data, a method combining the information entropy model and GOS model was used to analyse the effect of geographical similarity of those factors on landslide susceptibility assessment. The study led to the following conclusions:  
1. The RMSE of the sample is minimised when 0.015 is selected as the optimal threshold parameter. The AUC value of the GOS model increased by 2.81% to 8.92% compared to methods such as RF, LR, and RBFC, while achieving the lowest RMSE, indicating superior performance. Moreover, the GOS model with optimal threshold sample selection outperforms the BCS model by increasing its AUC value by 9.64%, reaching 0.846. This outcome enhanced the model's stability, decreased the sensitivity to historical data precision and demonstrated its strong applicability in landslide susceptibility assessments.
2. Most of the study area is prone to landslides, with non-susceptible zones comprising only 12.43% of the total area. Historical landslide points are primarily located in moderate- and high-susceptibility zones, where 606 landslides account for 92.24% of all occurrences. The moderate-susceptibility zone covers the largest area (3198.84 km\({}^{2}\)), whereas the high-susceptibility zone exhibits the highest landslide density at 0.15 points/km\({}^{2}\).
3. High-susceptibility areas are predominantly located in regions with high precipitation and low vegetation coverage. These areas have steep, predominantly south-facing slopes, high curvatures, strong runoff erosion capacities, and weak, soft rocks, making landslides more likely. Low susceptibility and non-susceptible areas are primarily flat regions with high vegetation coverage, located far from fault lines, with rock formations consisting mainly of hard granite and diorite complexes, weak runoff erosion capacities, and a low landslide likelihood.  
This study introduces the GOS model into landslide susceptibility assessment for the first time, combining it with the IV method. The GOS model overcomes the limitations of previous models, which depend heavily on the accuracy and distribution of historical data. It also improves the stability and reliability of sample data, offering an efficient, stable, and interpretable method for landslide risk assessment in complicated geological environments.  
Conceptualization: [PERSON], [PERSON] and [PERSON]; methodology: [PERSON], [PERSON] and [PERSON]; software: [PERSON] and [PERSON]; validation: [PERSON] and [PERSON]; formal analysis: [PERSON], [PERSON] and [PERSON]; investigation: [PERSON] and [PERSON]; resources: [PERSON] and [PERSON]; data curation: [PERSON] and [PERSON]; writing--original draft preparation: [PERSON] and [PERSON]; writing--review and editing: [PERSON], [PERSON] and [PERSON]; supervision: [PERSON]; project administration: [PERSON] and [PERSON]; funding acquisition: Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42071085).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to express our sincere appreciation to everyone who made a substantial contribution to the successful completion of our research project.  
The authors declare no conflicts of interest.