The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been l...The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been largely neglected.This study examined the effect of varying monitoring frequencies on the accuracy of displacement predictions by using the Baijiabao landslide in the Three Gorges Reservoir Area(TGRA)as a case study.We collected surface automatic monitoring data at different intervals,ranging from daily to monthly.The Ensemble Empirical Mode Decomposition(EEMD)algorithm was utilized to dissect the accumulated displacements into periodic and trend components at each monitoring frequency.Polynomial fitting was applied to forecast the trend component while the periodic component was predicted with two state-of-the-art neural network models:Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU).The predictions from these models were integrated to derive cumulative displacement forecasts,enabling a comparative analysis of prediction accuracy across different monitoring frequencies.The results demonstrate that the proposed models achieve high accuracy in landslide displacement forecasting,with optimal performance observed at moderate monitoring intervals.Intriguingly,the daily mean average error(MAE)decreases sharply with increasing monitoring frequency,reaching a plateau.These findings were corroborated by a parallel analysis of the Bazimen landslide,suggesting that moderate monitoring intervals of approximately 7 to 15 days are most conducive to achieving enhanced prediction accuracy compared to both daily and monthly intervals.展开更多
High-sensitivity monitoring solutions are crucial for early warning systems of earth structures. In this paper, we discuss the design and implementation of such systems for natural and engineered slopes using two case...High-sensitivity monitoring solutions are crucial for early warning systems of earth structures. In this paper, we discuss the design and implementation of such systems for natural and engineered slopes using two case studies. At the Gradenbach Observatory, one key element of the monitoring system is a large fiber optic strain rosette embedded in the slope. We demonstrate that the strain rosette can depict landslide deformations much earlier than geodetic sensors like GPS or total stations and is therefore well suitable for an early warning system. In a second application we report the construction of a reinforced earth structure using geogrids. A distributed fiber optic measurement system was installed to measure the current operating grade of the geogrids within the earth structure. About 2 km of Brillouin sensing cables were installed in the project area. It is demonstrated that the developed monitoring system is well suited for assessing the current state of health of reinforced earth structures.展开更多
基金supported by the Open Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area(China Three Gorges University)of the Ministry of Education(Grant Nos.2022KDZ14 and 2022KDZ15)the Open Fund of Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202304)+3 种基金the Science and Technology Project of Department of Natural Resources of Hubei Province(Grant No.ZRZY2024KJ15)the Natural Science Foundation of Hubei Province(Grant No.2022CFB557)the National Natural Science Foundation of China(Grant No.42107489)the 111 Project of Hubei Province(Grant No.2021EJD026)。
文摘The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been largely neglected.This study examined the effect of varying monitoring frequencies on the accuracy of displacement predictions by using the Baijiabao landslide in the Three Gorges Reservoir Area(TGRA)as a case study.We collected surface automatic monitoring data at different intervals,ranging from daily to monthly.The Ensemble Empirical Mode Decomposition(EEMD)algorithm was utilized to dissect the accumulated displacements into periodic and trend components at each monitoring frequency.Polynomial fitting was applied to forecast the trend component while the periodic component was predicted with two state-of-the-art neural network models:Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU).The predictions from these models were integrated to derive cumulative displacement forecasts,enabling a comparative analysis of prediction accuracy across different monitoring frequencies.The results demonstrate that the proposed models achieve high accuracy in landslide displacement forecasting,with optimal performance observed at moderate monitoring intervals.Intriguingly,the daily mean average error(MAE)decreases sharply with increasing monitoring frequency,reaching a plateau.These findings were corroborated by a parallel analysis of the Bazimen landslide,suggesting that moderate monitoring intervals of approximately 7 to 15 days are most conducive to achieving enhanced prediction accuracy compared to both daily and monthly intervals.
基金the Austrian Academy of Sciences(OeAW)for funding the landslide monitoring project for several yearsthe Austrian Federal Railways(OBB)for the funding of the geogrid monitoring project,especially the participating departments of OBB-Infrastruktur AG:Tunneling,Surveying and Data Management,Research and Development
文摘High-sensitivity monitoring solutions are crucial for early warning systems of earth structures. In this paper, we discuss the design and implementation of such systems for natural and engineered slopes using two case studies. At the Gradenbach Observatory, one key element of the monitoring system is a large fiber optic strain rosette embedded in the slope. We demonstrate that the strain rosette can depict landslide deformations much earlier than geodetic sensors like GPS or total stations and is therefore well suitable for an early warning system. In a second application we report the construction of a reinforced earth structure using geogrids. A distributed fiber optic measurement system was installed to measure the current operating grade of the geogrids within the earth structure. About 2 km of Brillouin sensing cables were installed in the project area. It is demonstrated that the developed monitoring system is well suited for assessing the current state of health of reinforced earth structures.
