## 5 Conclusions
In addition, during the rainy season from July to September each year in the monitoring area, the rainfall not only increases the water content of the rock and soil but also imposes significant loads on the shallow layers of landslides. It leads to a substantial decrease in the overall stability coefficient of the landslide distribution area and makes the landslide hazard enter a prone period. Therefore, during this period, the frequency of landslide monitoring should be increased, and exceptional optical cables with strong tensile and shear capabilities should be selected for combined monitoring to ensure the real-time acquisition of monitoring data from multiple underground sites of landslides. This ensures that immediate warnings can be issued before landslides occur to minimize the impact of landslide hazards. In addition, to improve the monitoring accuracy and reliability of large-scale landslides, it is necessary to comprehensively utilize the advantages of various mechanical, optical, and electrical monitoring technologies to build a comprehensive system of landslide monitoring and ensure the overall stability of the landslide system. However, considering the impact of extreme weather conditions such as typhoons and short-term heavy rainfall on monitoring systems, it is necessary to develop distributed, robust, durable, and stable landslide sensing monitoring technologies vigorously in the future. In particular, it is essential to strengthen the research and development of installation for deep landslide sensors to meet the survival rate of sensors under extreme conditions. Meanwhile, in actual monitoring, the reliability of monitoring data should be ensured by combining wavelet denoising and clustering analysis. Specifically, in monitoring landslides in the reservoir area, the time delay effect of landslide deformation caused by periodic changes in factors such as reservoir water level and rainfall needs to be considered. In the next step of reservoir bank landslide monitoring, the periodic displacement and trend displacement of the landslide should be comprehensively regarded. A disaster warning model based on full-cycle data machine learning should be established based on this. Through long-term data accumulation, the probability and degree of landslide hazard should be evaluated, and the potential instability of landslides in the region should be evaluated in the future. Based on this, landslide hazard monitoring and warning accuracy should be maximized to provide crucial technical support and a reference basis for disaster prevention and control. [PERSON] and [PERSON] designed the overall framework and conceived the idea of this paper; [PERSON], [PERSON], [PERSON] and [PERSON] conducted tests and organized test information; [PERSON], [PERSON] and [PERSON] completed the analysis and summary of the test progress and result; [PERSON] provided some suggestions on the structure of the paper; and [PERSON], [PERSON] and [PERSON] wrote the paper. 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 (42377200); the Central Government Guided Local Science and Technology Development Fund (226Z5404G); the Natural Science Foundation of Hebei Province, China (D2022508002); the Fundamental Research Funds for the Central Universities (3142019011); the Hebei IoT Monitoring Engineering Technology Innovation Center (21567693H); the Engineering Research Center of Zero-carbon and Negative-carbon Technology in Depth of Mining Areas, Ministry of Education (China University of Mining and Technology) (2023-0014); and the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP2023K021). The data are available from the corresponding author on reasonable request. The authors would like to thank [PERSON] and [PERSON] for their assistance in processing data and images, and thank [PERSON] for providing more detailed geological data and drone monitoring results image of the monitoring area.