Inverse Estimation on Trigger Factors of Simultaneous Slope Failures with Purification of Training Data Sets

This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂. Due to difficulties in pixel-by-pixel observations of trigger factors, as one of the measures, the authors had proposed an inverse analysis algorithm on trigger factors based on SEM （structural equation modeling）. Through a measurement equation, the trigger factor is inversely estimated, and a TFI （trigger factor influence） map can be also produced. As a subsequence subject, a purification procedure of training data set should be constructed to improve the accuracy of TFI map which depends on the representativeness of given training data sets of different types of slope failures. The proposed procedure resamples the matched pixels between original groups of past slope failures （i.e., surface slope failures, deep-seated slope failures, landslides） and classified three groups by K-means clustering for all pixels corresponding to those slope failures. For all cases of three types of slope failures, the improvement of success rates with respect to resampled training data sets was confirmed. As a final outcome, the differences between TFI maps produced by using original and resampled training data sets, respectively, are delineated on a DIF map （difference map） which is useful for analyzing trigger factor influence in terms of ＂risky- and safe-side assessment＂ sub-areas with respect to ＂different types of simultaneous slope failures＂.

Development characteristics and failure modes of reactivated ancient landslides in the Sichuan–Tibet transportation corridor,China

The risk of reactivated ancient landslides in the Sichuan–Tibet transportation corridor in China is significantly increasing,primarily driven by the intensification of engineering activities and the increased frequency of extreme weather events.This escalation has resulted in a considerable number of fatalities and extensive damage to critical engineering infrastructure.However,the factors contributing to the reactivation and modes of destruction of ancient landslides remain unknown.Therefore,it is imperative to systematically analyze the developmental characteristics and failure modes of reactivated ancient landslides to effectively mitigate disaster risks.Based on a combination of data collection,remote sensing interpretation,and field investigations,we delineated the developmental attributes of typical ancient landslides within the study area.These attributes encompass morphological and topographic aspects,material composition,and spatial structure of ancient landslides.Subsequently,we identified the key triggers for the reactivation of ancient landslides,including water infiltration,reservoir hydrodynamics,slope erosion,and excavation,by analyzing representative cases in the study area.Reactivation of ancient landslides is sometimes the result of the cumulative effects of multiple predisposing factors.Furthermore,our investigations revealed that the reactivation of these ancient landslides primarily led to local failures.However,over extended periods of dynamic action,the entire zone may experience gradual creep.We categorized the reactivation modes of ancient landslides into three distinct types based on the reactivation sequences:progressive retreat,backward thrusting,and forward pulling–backward thrusting.This study is of great significance for us to identify ancient landslides,deepen our understanding of the failure modes and risks of reactivated ancient landslides on the eastern margin of the Tibetan Plateau,and formulate effective disaster prevention and mitigation measures.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China

The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitigation.In order to enhance the accuracy of VD prediction,a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising(EEMD-ITD),Differential evolutions—Shuffled frog leaping algorithm(DE-SFLA)and Least squares support vector machine(LSSVM)is proposed.The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD;then,ITD is applied for redundant information denoising on special sub-series,and the denoised deformation is divided into the trend and periodic deformation components.Meanwhile,several relevant triggering factors affecting the VD are considered,from which the input features are extracted by Grey relational analysis(GRA).After that,DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs.Ultimately,the two individual forecast components are reconstructed to obtain the final predicted values.Two VD series monitored in Xiluodu reservoir region are utilized to verify the proposed model.The results demonstrate that:(1)Compared with Discrete wavelet transform(DWT),better denoising performance can be achieved by EEMD-ITD;(2)Using GRA to screen the optimal input features can effectively quantify the deformation response relationship to the triggering factors,and reduce the model complexity;(3)The proposed hybrid model in this study displays superior performance on some compared models(e.g.,LSSVM,Backward Propagation neural network(BPNN),and DE-SFLA-BPNN)in terms of forecast accuracy.

Joint Effects and Spatiotemporal Characteristics of the Driving Factors of Landslides in Earthquake Areas

Understanding the joint effects of earthquakes and driving factors on the spatial distribution of landslides is helpful for targeted disaster prevention and mitigation in earthquake-prone areas.By far,little work has been done on this issue.This study analyzed the co-seismic landslide of the Ms8.0 Wenchuan earthquake in 2008 and 2014.The joint effects and spatiotemporal characteristics of the driving factors in seismic regions were revealed.Results show that(a)between 2008 and 2014,the dominant driving-factor for landslides has changed from earthquake to rock mass;(b)driving factors with weak driving force have a significant enhancement under the joint effects of other factors;(c)the joint effects of driving factors and earthquake decays with time.The study concluded that the strong vibration of the Wenchuan earthquake and the rock mass strength are the biggest contributors to the spatial distribution of landslides in 2008 and 2014,respectively.It means that the driving force of the earthquake is weaker than that of the rock mass after six years of the Wenchuan earthquake.Moreover,the landslide spatial distribution can be attributed to the joint effects of the Wenchuan earthquake and driving factors,and the earthquake has an enhanced effect on other factors.

Studies on Garden Path Phenomenon in English

Garden path phenomenon is a term that originated from psycholinguistic field. As a special temporary or local ambiguity in language processing, it has been widely explored and studied from aspects of semantics, syntax, pragmatics as well as psycholinguistics and cognitive linguistics over the years, since it was first put forward by Bever in the 1970s. The research of garden path phenomenon is of considerable significance both theoretically and practically. This paper is designed to explore the garden path phenomenon in English through literature and examples. Some basic concepts and main triggering factors of garden path phenomenon are analyzed. This paper explores the triggering factors of garden path phenomenon from the aspects of syntax, semantics, and pragmatics. The major finding is that garden path phenomenon can help learners understand the operation of human language processing mechanism and improve their abilities to deal with garden path sentences, which is of great benefits to learn and understand English language.

Effective Measure for Accident Prevention Onboard Sea Vessels—Improvements on 4M4E Analysis

The 4M4E analysis is a type of root-cause analysis that can multilaterally pinpoint the trigger factors of an accident or disaster using its analytic capabilities and can clarify various countermeasures against each trigger factor.This study aims to reduce the number of vessel accidents and disasters involving seafarers by improving the practical use of 4M4E analysis.Vessel accidents or disasters involving seafarers,related to a mooring line,sometimes result in a fatality;therefore,this research area has attracted international attention.In consideration of this,we devised an analysis method for accidents involving a mooring line by adding prediction to the 4Ms of 4M4E,having first extracted the potential causes of an accident through brainstorming.The 4M4E+P analysis could obtain additional trigger factors that were not revealed in the 4M4E analysis.Thus,a measure of adopting these newly acquired trigger factors was evaluated.In addition,it is thought that 4M4E+P analysis can reduce the risk of vessel accidents and disasters involving seafarers.

Improved Kalman filter method considering multiple factors and its application in landslide prediction

Landslides,seriously threatening human lives and environmental safety,have become some of the most catastrophic natural disasters in hilly and mountainous areas worldwide.Hence,it is necessary to forecast landslide deformation for landslide risk reduction.This paper presents a method of predicting landslide displacement,i.e.,the improved multi-factor Kalman filter(KF)algorithm.The developed model has two advantages over the traditional KF approach.First,it considers multiple external environmental factors(e.g.,rainfall),which are the main triggering factors that may induce slope failure.Second,the model includes random disturbances of triggers.The proposed model was constructed using a time series which consists of over 16-month of data on landslide movement and precipitation collected from the Miaodian loess landslide monitoring system and nearby meteorological stations in Shaanxi province,China.Model validation was performed by predicting movements for periods of up to 7 months in the future.The performance of the developed model was compared with that of the improved single-factor KF,multi-factor KF,multi-factor radial basis function,and multi-factor support vector regression approaches.The results show that the improved multi-factor KF method outperforms the other models and that the predictive capability can be improved by considering random disturbances of triggers.