## 6 Future Research Directions  
### Dynamic Landslide Susceptibility Mapping and Prediction  
The integration of time-series analysis into susceptibility mapping is crucial for capturing the temporal evolution of landslide susceptibility, which is inherently dynamic and influenced by factors such as vegetation growth, land use changes, and climate patterns. With the ability to monitor deformation over time, InSAR plays a vital role in introducing the temporal dimension into LSM. Recent studies have revealed that the landslide deformation typically exhibits seasonal patterns attributed to concentrated heavy precipitation [148; 149]. Therefore, future research could incorporate InSAR-derived deformation to explore susceptibility variations across different timescales, such as seasonal, monthly, or even weekly, thereby reflecting the dynamic changes in susceptibility and their correlation with weather patterns and environmental factors. In addition, it may also be viable to integrate other dynamic factors that contribute to susceptibility, such as rainfall, groundwater level fluctuations, soil moisture, and vegetation conditions. These variables can be derived from various sources, including remote sensing observations, hydrological models, and in situ measurements. A comprehensive susceptibility mapping framework that synthesizes both spatial and temporal dynamics will significantly improve landslide prediction capabilities, thereby strengthening early warning systems and risk mitigation strategies.  
Nonetheless, dynamic LSM typically focuses on assessing spatiotemporal variations in susceptibility and general risk at the regional scale. However, for specific landslides, incorporating displacement patterns over time allows for a more precise and near real-time understanding of slope behavior, as well as enabling dynamic prediction. Although significant progress has been made in displacement prediction based on InSAR, with several studies successfully establishing quantitative relationships between factors' variations (e.g., rainfall and water levels) and displacement [95; 150], failure time prediction remains more challenging. Current InSAR-based failure time prediction methods primarily rely on creep theory, as discussed in Section 4.2.3. These kinds of methods require clear identification and monitoring of the accelerated phase of landslides, but slopes might be influenced by multiple triggering factors such as rainfall, reservoir water level fluctuations, and human activities like road construction and mining. Some of these factors may introduce periodic behavior to the natural deformation trend, while others may disrupt the natural deformation process and even alter the stages of a landslide's development, resulting in the low accuracy of failure time predictions based on creep theory. While existing studies have explored the impact of deformation stage division on landslide prediction and early warning [151], how to integrate these triggering factors to comprehensively analyze the failure characteristics of specific landslides is also worth exploring in future failure time studies. The application of data-driven models for failure time prediction might be a promising direction. By incorporating these triggering factors and dynamically updating them, along with accurate time-series displacement data to analyze deformation stages, this evolving approach could significantly enhance the accuracy and timeliness of failure time predictions, providing more reliable early warning capabilities.