# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 6 Conclusions and Future Perspectives  
This study reviewed the fundamentals and state-of-the-art applications of the AE technique for landslide monitoring and early warning. Key conclusions and future outlooks are as follows. 1. The AE method exploits the high-resolution, non-contact sensing of elastic emissions generated by evolving subsurface damage as soils and rocks deform. While laboratory investigations have established qualitative and quantitative understandings of the failure mechanisms, signal attenuation remains the foremost challenge for long-range field implementations. To address this, ongoing innovations are optimizing embedded sensor configurations, waveguides, and hardware designs tailored to the specific propagation conditions. 2. Advancements in MEMS/optical sensing, data acquisition systems, and wireless automated networks are transforming monitoring capabilities towards higher fidelities over extensive volumes with continuous coverage. However, validating the evolving quantification schemes under diverse geological settings remains necessary to establish their reliability and widespread applicability. 3. The integration of AE monitoring with multimodal sensing techniques, such as surface deformation measurements and hydrological monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. By leveraging the complementary attributes of individual methods, these hybrid monitoring frame works overcome the limitations of isolating single modalities, thereby optimizing the monitoring scope and improving the accuracy of predictive models. In light of these findings, this review recommends future research to explore machine learning-based analysis techniques for AE signals, which may significantly enhance predictive capabilities and improve data interpretation. Considering the complexity of AE data and their correlations with various landslide-related factors, data-driven approaches can help uncover intricate signal features and patterns that are difficult to detect through traditional analysis methods, enabling the more accurate prediction of landslide occurrences. Additionally, the development of coordinated multi-scale monitoring frameworks, optimized through virtual simulations, can further advance the integration of AE with other sensing technologies for enhanced landslide forecasting capabilities. Overall, the AE technique holds great promise for achieving the long-standing goal of comprehensively characterizing subsurface slope responses through quantitative early precursory detections. This capability can substantially augment disaster mitigation efforts by providing reliable early warning systems. Continued research and development, particularly in the context of climate change and the increasing frequency of extreme weather events, is essential for strengthening the effectiveness of AE as a robust solution for landslide prediction. Conceptualization, J.S. and J.L. ([PERSON]); methodology, J.L. ([PERSON]); software, S.L. ; validation, J.L. ([PERSON]), H.W. and F.W. ; formal analysis, J.S. ; investigation, J.S. and J.L. ([PERSON]); resources, F.W. ; data curation, J.L. ([PERSON]) and S.L. ; writing--original draft preparation, J.S. and S.L. ; writing--review and editing, H.W. and F.W. ; visualization, [PERSON] ([PERSON]); supervision, H.W. ; project administration, J.S. ; funding acquisition, H.W. 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 numbers 52278436 and 52208426; the National Key R&D Program of China, grant number 2023 YFB2603500; the Scientific Research Project of Hunan Provincial Department of Education for Key Research, grant number 24A0241; the Science and Technology Innovation Program of Hunan Province, grant number 2022 RC1024; and the Foundation of Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road and Traffic Safety of Ministry of Education, grant number kfj220402. Not applicable.