Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

Model tests and numerical analysis of emergency treatment of cohesionless soil landslide with quick-setting polyurethane

Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.

Influence of material characteristics on the failure mode and process of landslide dam

Landslide dams,as frequent natural hazards,pose significant risks to human lives,property,and ecological environments.The grading characteristics and density of dam materials play a crucial role in determining the stability of landslide dams and the potential for dam breaches.To explore the failure mechanisms and evolutionary processes of landslide dams with varying soil properties,this study conducted a series of flume experiments,considering different grain compositions and material densities.The results demonstrated that grading characteristics significantly influence landslide dam stability,affecting failure patterns,breach processes,and final breach morphologies.Fine-graded materials exhibited a sequence of surface erosion,head-cut erosion,and subsequent surface erosion during the breach process,while well-graded materials typically experienced head-cut erosion followed by surface erosion.In coarse-graded dams,the high permeability of coarse particles allowed the dam to remain stable,as inflows matched outflows.The dam breach model experiments also showed that increasing material density effectively delayed the breach and reduced peak breach flow discharge.Furthermore,higher fine particle content led to a reduction in the residual dam height and the base slope of the final breach profile,although the relationship between peak breach discharge and the content of fine and coarse particles was nonlinear.To better understand breach morphology evolution under different soil characteristics and hydraulic conditions,three key points were identified—erosion point,control point,and scouring point.This study,by examining the evolution of failure patterns,breach processes,and breach flow discharges under various grading and density conditions,offers valuable insights into the mechanisms behind landslide dam failures.

Using fracture mechanics method to analyze the failure mechanism and equilibrium equation of interfacial loess-mudstone landslides

Loess-mudstone landslides are common in the Loess Plateau.Investigations into the mechanical theory of loess-mudstone landslides have become a challenging undertaking due to the distinctive interfacial properties of loess-mudstone and the unique water sensitivity characteristics of mudstone.Hence,it is imperative to develop innovative mechanical models and mathematical equations specifically tailored to loess-mudstone landslides.In this study,we analyze the fracture mechanism of the loess-mudstone sliding zone using plastic fracture mechanics and develop a unique fracture yield model.To calculate the energy release rate during the expansion of the loess-mudstone interface tip region,the shear fracture energy G is applied,which reflects both the yield failure criterion and the fracture failure criterion.To better understand the instability mechanism of loess-mudstone landslides,equilibrium equations based on G are established for tractive,compressive,and tensile loess-mudstone landslides.Based on the equilibrium equation,the critical length Lc of the sliding zone can be used for the safety evaluation of loess-mudstone landslides.In this way,this study proposes a new method for determining the failure mechanism and equilibrium equation of loessmudstone landslides,which resolves their starting mechanism,mechanical equilibrium equations,and safety evaluation indicators,thus justifying the scientific significance and practical value of this research.

Forecasting step-like landslide displacement through diverse monitoring frequencies

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.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

Spatial distribution of landslides in response to the geomorphometric constraints in Darma Valley,Kumaun Himalaya

The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.

Long-term monitoring of active large-scale landslides for non-structural risk mitigation-integrated sensors and web-based platform

Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.

Failure process and monitoring data of an extra-large landslide at the Nanfen Open-pit Iron Mine

An extra-large landslide occurred on June 19,2021,on the footwall slope of the Nanfen Open-pit Iron Mine in Liaoning Province,China,with a volume of approximately 1.2×107 m3.To elucidate the causative factors,development process,and destructive mechanisms of this catastrophic landslide,comprehensive field tests,investigations,and laboratory experiments were conducted.Initially,the heavily weathered rock mass of the slope was intersected by faults and joint fissures,facilitating rainwater infiltration.Moreover,the landslide contained a substantial clay mineral with highly developed micro-cracks and micro-pores,exhibiting strong water-absorption properties.As moisture content increased,the rock mass underwent softening,resulting in reduced strength.Ultimately,continuous heavy rainfall infiltration amplified the slope's weight,diminishing the weak structural plane's strength,leading to fracture propagation,slip plane penetration,and extensive tensile-shear and uplift failure of the slope.The study highlights poor geological conditions as the decisive factor for this landslide,with continuous heavy rainfall as the triggering factor.Presently,adverse environmental factors persistently affect the landslide,and deformation and failure continue to escalate.Hence,it is imperative to urgently implement integrated measures encompassing slope reinforcement,monitoring,and early-warning to real-time monitor the landslide's deformation and deep mechanical evolution trends.

Investigating the reactivation of historical landslides during the 2022 Luding M_(S)6.8 earthquake

On September 5,2022,a strong earthquake with a magnitude of MS6.8 struck Luding County in Sichuan Province,China,triggering thousands of landslides along the Dadu River in the northwest-southeast(NW-SE)direction.We investigated the reactivation characteristics of historical landslides within the epicentral area of the Luding earthquake to identify the initiation mechanism of earthquake-induced landslides.Records of the two newly triggered and historical landslides were analyzed using manual and threshold methods;the spatial distribution of landslides was assessed in relation to topographical and geological factors using remote sensing images.This study sheds light on the spatial distribution patterns of landslides,especially those that occur above historical landslide areas.Our results revealed a similarity in the spatial distribution trends between historical landslides and new ones induced by earthquakes.These landslides tend to be concentrated within a range of 0.2 km from the river and 2 km from the fault.Notably,both rivers and faults predominantly influenced the reactivation of historical landslides.Remarkably,the reactivated landslides are characterized by their small to medium size and are predominantly situated in historical landslide zones.The number of reactivated landslides surpassed that of previously documented historical landslides within the study area.We provide insights into the critical factors responsible for historical landslides during the 2022 Luding earthquake,thereby enhancing our understanding of the potential implications for future co-seismic hazard assessments and mitigation strategies.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

Exploring mechanism of hidden,steep obliquely inclined bedding landslides using a 3DEC model:A case study of the Shanyang landslide in Shaanxi Province,China

Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures(also referred to as obliquely inclined bedding slopes),where the apparent dip sliding is not readily visible.This phenomenon has become a focal point in landslide research.Yet,there is a lack of studies on the failure modes and mechanisms of hidden,steep obliquely inclined bedding slopes.This study investigated the Shanyang landslide in Shaanxi Province,China.Using field investigations,laboratory tests of geotechnical parameters,and the 3DEC software,this study developed a numerical model of the landslide to analyze the failure process of such slopes.The findings indicate that the Shanyang landslide primarily crept along a weak interlayer under the action of gravity.The landslide,initially following a dip angle with the support of a stable inclined rock mass,shifted direction under the influence of argillization in the weak interlayer,moving towards the apparent dip angle.The slide resistance effect of the karstic dissolution zone was increasingly significant during this process,with lateral friction being the primary resistance force.A reduction in the lateral friction due to karstic dissolution made the apparent dip sliding characteristics of the Shanyang landslide more pronounced.Notably,deformations such as bending and uplift at the slope’s foot suggest that the main slide resistance shifts from lateral friction within the karstic dissolution zone to the slope foot’s resistance force,leading to the eventual buckling failure of the landslide.This study unveils a novel failure mode of apparent dip creep-buckling in the Shanyang landslide,highlighting the critical role of lateral friction from the karstic dissolution zone in its failure mechanism.These insights offer a valuable reference for mitigating risks and preventing disasters related to obliquely inclined bedding landslides.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Electrical Characteristics of Tangjiawan Landslide in Lixian, Sichuan

A wide range of terrain features and landforms,which are exemplified by intricate geological formations and diverse rock compositions,are found in the western mountainous regions of China.These areas frequently encounter geological disasters.As one of the natural disasters,landslides lead to considerable loss of human life and property.Considering mitigation of the losses caused by landslide disasters,a necessary measure for disaster prevention and mitigation involves conducting detailed investigations and monitoring of landslides,which is also the cornerstone of landslide warning.This study compares and analyzes the feasibility of the magnetotelluric detection method for landslides using the results of engineering geological surveys and landslide monitoring.The study aims to address the scientific problem of the validity of using magnetotelluric methods to detect landslide development processes.The Tangjiawan landslide signal on the left side of the K94+000~K94+145 section of the Wenma Expressway is analyzed by employing engineering geological survey,magnetotelluric detection,landslide monitoring,landslide analysis,and other methods.Analysis results provide the static electrical characteristics of lithology,structure,and groundwater,as well as the dynamic electrical characteristics of landslide development.This study focuses on analyzing the relationship between the methods of magnetotelluric detection and engineering geological surveys and the results of landslide monitoring.The workflow and methods for data collection,processing,inversion,interpretation,and analysis using the magnetotelluric method to detect the dynamic development process of landslides are presented in the conclusion.Preliminary conclusions indicate a strong correlation between the dynamic changes in magnetotelluric wave impedance with the surface displacement of landslides and the dynamic changes in groundwater.The use of the magnetotelluric method for landslide detection and monitoring is a feasible example.The research results can offer certain technical references for the detection and monitoring of landslides using magnetotelluric methods and also provide references and guidance for the selection of diversified landslide monitoring methods in the future.

GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas

Rainwater runoff that does not infiltrate the soil during heavy rainfall may increase slope instability. The effect of runoff is usually neglected in conventional rainfall-induced slope failure analysis to simplify the model. To analyze the effect of runoff on slope stability, this study simultaneously simulated the effects of surface runoff and rainfall infiltration on bank slopes in the Three Gorges Reservoir Area. A shallow slope failure method that can be used to analyze runoff was proposed based on the modified Green-Ampt model, the simplified Saint-Venant model, and the infinite slope model. In this model, the modified Green–Ampt model was used to estimate the rainfall infiltration capacity and the wetting front depth. The eight-flow(D8) method and the simplified Saint-Venant model were selected to estimate the distribution of runoff. By considering the wetting front depth as the slip surface depth, the factor of safety of the slope could be determined using the infinite slope stability model. A comparison of the different models reveals that runoff can escalate the instability of certain slopes, causing stable slopes to become unstable. Comparison of the unstable areas obtained from the simulation with the actual landslide sites shows that the model proposed in this study can successfully predict landslides at these sites. The slope instability assessment model proposed in this study offers an alternative approach for estimating high-risk areas in large mountainous regions.

Identification and distribution of 13003 landslides in the northwest margin of Qinghai-Tibet Plateau based on human-computer interaction remote sensing interpretation

The periphery of the Qinghai-Tibet Plateau is renowned for its susceptibility to landslides.However,the northwestern margin of this region,characterised by limited human activities and challenging transportation,remains insufficiently explored concerning landslide occurrence and dispersion.With the planning and construction of the Xinjiang-Tibet Railway,a comprehensive investigation into disastrous landslides in this area is essential for effective disaster preparedness and mitigation strategies.By using the human-computer interaction interpretation approach,the authors established a landslide database encompassing 13003 landslides,collectively spanning an area of 3351.24 km^(2)(36°N-40°N,73°E-78°E).The database incorporates diverse topographical and environmental parameters,including regional elevation,slope angle,slope aspect,distance to faults,distance to roads,distance to rivers,annual precipitation,and stratum.The statistical characteristics of number and area of landslides,landslide number density(LND),and landslide area percentage(LAP)are analyzed.The authors found that a predominant concentration of landslide origins within high slope angle regions,with the highest incidence observed in intervals characterised by average slopes of 20°to 30°,maximum slope angle above 80°,along with orientations towards the north(N),northeast(NE),and southwest(SW).Additionally,elevations above 4.5 km,distance to rivers below 1 km,rainfall between 20-30 mm and 30-40 mm emerge as particularly susceptible to landslide development.The study area’s geological composition primarily comprises Mesozoic and Upper Paleozoic outcrops.Both fault and human engineering activities have different degrees of influence on landslide development.Furthermore,the significance of the landslide database,the relationship between landslide distribution and environmental factors,and the geometric and morphological characteristics of landslides are discussed.The landslide H/L ratios in the study area are mainly concentrated between 0.4 and 0.64.It means the landslides mobility in the region is relatively low,and the authors speculate that landslides in this region more possibly triggered by earthquakes or located in meizoseismal area.

Landslide Damming Threats Along the Jinsha River,China

The 2280 km long Jinsha River has been blocked at least four times in the past 30 years.A landslide dam-ming hazard chain can endanger communities and infrastructures hundreds of kilometers downstream from the damming site in alpine gorges.Past damming events have resulted in severe consequences,demanding a thorough assessment of damming threats along the entire Jinsha River.This study digitizes the Jinsha River and visualizes its topographic,tectonic,hydrologic,and climate characteristics in detail.A two-stage full-probability method is proposed for assessing the damming threats along this river,mak-ing it possible to identify potential damming hotspots and high-priority zones for hazard mitigation.It is found that the upper reach of the Jinsha River poses the greatest damming threat,and the threat level gradually decreases downstream.Approximately 33.4%,36.7%,20.5%,and 9.4%of the entire length of the Jinsha River are classified as low,moderate,high,and very high threat levels,respectively.Compared with existing hydropower projects,future projects in the upper reach are more likely to be exposed to landslide damming.We highlight the value of basin-scale spatial threat analysis and envisage that our findings will promote more targeted local-scale risk assessments for potential damming hot-spots.These outcomes provide the basis for managing the risks of river damming and hydropower infras-tructure along the Jinsha River.

Spatial distribution characteristics and influence factor analysis of landslides——case study of the Hanwang area in Qinba Mountains

The geological hazards of landslides in Hanwang Town, Ziyang County, Ankang City, Shaanxi Province, have emerged. Yet, the current understanding of the spatial distribution characteristics and influencing factors of landslides in this area remains unclear. Combining the results of remote sensing interpretation and field investigation, seven influencing factors, namely, elevation, slope direction, slope gradient, distance from rivers, distance from faults, engineering geologic lithology, and distance from roads, are selected for the study. The distribution characteristics of landslides in each influencing factor and the influence of the resolution of the Digital Elevation Model(DEM) on the results are statistically and analytically analyzed. Furthermore, two highrisk landslides within the study area were subjected to comprehensive analysis, integrating the findings from drilling and other field investigations in order to examine their deformation mechanisms. Based on this analysis,the following conclusions were derived:(1) 34 landslides in the study area, mainly small earth landslides, with a distribution density of 0.42/km~2, threatening 414 people and property of about 55.87 million Yuan.(2)The landslides in the study area easily occur in the <400 m elevation range;the landslides are developed in all slope directions, the gradient is mainly concentrated in the range of 10°–40°, the distribution density of the landslides is higher in the closer distance from the river and the faults(0–200 m), the landslide-prone strata are mainly the softer and weaker metamorphic rocks, and the landslides are mainly around roads.(3) The resolution of the DEM should be selected based on the specific conditions of the study area, the requirements of the investigation, and the scale of the landslide. Opting for an appropriate DEM resolution is advantageous for understanding the patterns of landslides and conducting risk assessments in the region.(4) The Zhengjiabian landslide is a traction Landslide. The landslide body is a binary structure of gravel soil and slate weathering layer, and the damage process can be divided into three stages:(1)damage to the leading edge and stress release,(2)continuous creep and cracking,(3)rainfall infiltration and damage. The predominant slope material in the Brickyard landslide comprises clay, and the landslide is triggered by a combination of the traction effect resulting from the excavation at the slope's base and the nudging effect caused by the stacking load of the brick factory. Additionally, the Brickyard landslide exhibits persistent creep deformation. The study results provide a scientific basis for disaster prevention and mitigation in the Hanwang Township area.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.