Landslide Geological Disasters Research Articles

Landslide susceptibility assessment using deep learning considering unbalanced samples distribution

Landslide susceptibility assessment (LSA) is fundamental for managing landslide geological disasters. This study presents a deep learning approach (DNN-MSFM) designed to enhance LSA modeling, particularly addressing limitations caused by the unbalanced distribution of data samples in applied datasets. DNN-MSFM approach combines a deep neural network (DNN) and a mean squared false misclassification loss function (MSFM) to handle unbalanced samples from the algorithmic perspective. The model's performance was evaluated on an unbalanced dataset containing mapping units' records of 293 landslide samples and 653 non-landslide samples from the Baota District, China. Its effectiveness was assessed through statistical metrics and compared against DNN and Support Vector Machine (SVM) basic models. The results demonstrated a significant performance enhancement from the DNN-MSFM (OverallAccuracy = 0.889 and area under the receiver operating characteristic curve (AUC) = 0.84), indicating its effectiveness in learning the underlying landslide susceptibility features and demonstrating its ability to provide improved predictions even in areas with unbalanced landslide samples. Moreover, the study emphasizes the importance of considering balanced loss functions in training DNN under various imbalance degrees and contributes to expanding the applicability of DNN in LSA modeling. Also, this study builds a foundation for further enhancements of deep learning methods for geological disaster assessments.

Exploring the geological structure of a large-scale landslide using semi-airborne TEM in the Karst area of southwest China

Abstract The karst strata in the southwestern mountainous areas of China are extensively developed, creating a fragile geological environment. Landslide geological disasters occur frequently in these areas due to high annual rainfall, concentrated time periods, and frequent human engineering activities. Conventional ground geophysical methods face challenges in complex terrains, making it difficult to quickly and reliably obtain underground structures in landslide-prone areas. The semi-airborne transient electromagnetic method (SATEM) combines ground-based transmission and airborne reception of electromagnetic responses. This method, characterized by large emission magnetic moment and rapid data collection in the air, offers advantages in detecting deep geological structures in complex terrain areas. This article presents the application of a newly developed loop source SATEM system, integrated with multi-rotor unmanned aerial vehicles (UAVs), to conduct deep geological structure detection experiments on a large-scale landslide. The inversion results clearly depict the spatial distribution of the Feixianguan Formation and Longtan Formation, as well as vertical fractures developed in the strata due to underground coal mining. The detection test demonstrates the applicability of the SATEM method in detecting underground structures in Karst Plateau special landform areas, providing a new detection approach for similar regions.

Risk Assessment of Geological Landslide Hazards Using D-InSAR and Remote Sensing

Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters.

Research on landslide susceptibility prediction model based on LSTM-RF-MDBN.

The occurrence of landslide disasters causes huge economic losses and casualties. Although many achievements have been made in predicting the probability of landslide disasters, various factors such as the scale and spatial location of landslide geological disasters should still be fully considered. Further research on how to quantitatively characterize the susceptibility of landslide geological disasters is necessarily important. To this end, taking the Wenchuan earthquake as the research area and extracting eight influencing factors, including terrain information entropy (Ht), lithology, distance from rivers, distance from faults, vegetation coverage (NDVI), distance from roads, peak ground motion acceleration (PGA), and annual rainfall, a landslide susceptibility prediction model was hereby established based on LSTM-RF-MDBN, a landslide susceptibility prediction map was drawn, and the spatial distribution characteristics of landslide disasters were analyzed. The results showed that (1) LSTM had good prediction results for the eight influencing factors, with an average prediction accuracy of 85%; (2) compared with models such as DNN and LR for predicting landslide disaster points, the AUC value of RF for predicting landslide point positions reached 0.88, presenting a higher accuracy compared to other models; (3) the AUC value of the landslide susceptibility prediction model based on LSTM-RF-MDBN reached 0.965, which had a high accuracy in predicting landslide susceptibility. Overall, the research results can provide a scientific basis for selecting the best strategy for landslide disaster warning, prevention, and mitigation.

Construction and application of a multilevel geohazard domain ontology: A case study of landslide geohazards

The occurrence of geohazards entails sudden, unpredictable, and cascading effects, with numerous conceptual frameworks and intricate spatiotemporal relationships existing between hazard events. Presently, the absence of a unified mechanism for describing and expressing geohazard knowledge poses substantial challenges in terms of sharing and reusing domain-specific knowledge pertaining to geohazards. Therefore, it is imperative to address the issue of constructing a cohesive descriptive model that facilitates the sharing and reuse of geohazard knowledge. In this study, we propose a multilayered ontology construction method tailored specifically for the domain of landslide geological hazards. By comparing existing methods, we establish a hierarchical structure and expression framework for the geological hazard ontology. Notably, our approach seamlessly integrates the conceptual and semantic layers in the relationship description at each level, enabling association representation of hazard data across multiple tiers. We define essential concepts and attributes related to landslide geological hazards, along with their respective interrelationships. To achieve effective knowledge sharing and reuse, we model the ontology of the landslide geological disaster domain using the Web Ontology Language (OWL). This modeling approach serves as a powerful tool that facilitates the sharing and reuse of disaster-related knowledge. Finally, we verify the method's validity and reliability by employing illustrative case studies. The results demonstrate that the proposed approach imposes an affordable workload on human resources. Additionally, the foundational domain ontology significantly enhances information retrieval performance, thereby yielding satisfactory outcomes.

Shrinkage Characteristics and Microstructure Evolution of Yili Loess under Different Wetting and Drying Cycles

The loess in Yili Valley is prone to landslides in the rainy season. We studied the influence law of shrinkage and the microstructure of the loess in Yili Valley under different wetting and drying cycles. Considering the climatic conditions and sampling depth of the study area, shrinkage tests were carried out under six kinds of dry and wet cycling paths. The fracture changes and shrinkage characteristics of the loess under different dry–wet cycling times were analyzed, and the deformation characteristics of the loess during the process of water-loss shrinkage under the dry–wet cycling conditions were discussed. The results show that (1) there is an exponential relationship between the number of dry and wet cycles and the final shrinkage rate. The influence of dry and wet cycles on the final shrinkage rate is significant in the early stage. (2) With the increase in the number of dry and wet cycles, the decline in the final shrinkage rate decreases, and the final shrinkage rate and shrinkage coefficient of soil also show a decreasing trend, while the soil sample area first increases and then gradually decreases, and the surface shrinkage cracking is gradually stable. The surface porosity tends to increase. (3) Under the action of repeated wet expansion and dry contraction, irregular cracks are produced inside the soil body, which leads to the increase in soil permeability, reduces the strength of the soil body, reflects the phenomenon of strength deterioration, and thus indicates the stability of loess slopes. The research results of this paper can provide an important parameter basis for the prevention and control of loess landslide geological disasters in Yili Valley.

Research on a BP Neural Network Slope Safety Coefficient Prediction Model Based on Improved Sparrow Algorithm Optimization

Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured. This work proposes an improved sparrow search algorithm to optimize the slope safety factor prediction model (ISSA–BP) of a BP neural network, through an improvement in two aspects: introducing dynamic weight factors and reverse learning strategies to realize adaptive searches. The optimal value improves a defect in the traditional model, preventing it from easily falling into the local minimum. First, combined with 352 sets of actual slope data, three machine learning models were used to predict the safety factor of the slope. Then, the accuracy index was used for evaluation. Compared with other models, the MAPE, RMSE, and R2 of the ISSA-BP model were 1.64%, 0.0296, and 0.99, respectively, and the error was reduced by 78% compared with the BP neural network, showing better accuracy. Finally, the three models were applied to the slope stability analysis of Tianbao Port in Wenshan Prefecture. The research shows that the predicted value of the ISSA–BP model was the closest to the actual safety factor, which verified the experimental results. The improved ISSA–BP model can effectively predict the safety factor of slopes under different conditions, and it provides a new technology for slope disaster warning and control.

Landslide Susceptibility Zoning in Yunnan Province Based on SBAS-InSAR Technology and a Random Forest Model

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.

A Slope Structural Plane Extraction Method Based on Geo-AINet Ensemble Learning with UAV Images

In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the slope are first used to generate a dense point cloud through a pipeline of SfM and PMVS; then, the multiple geological semantics, including color and texture from the image and local geological occurrence and surface roughness from the dense point cloud, are integrated with Geo-AINet for ensemble learning to obtain a set of semantic blocks; finally, the accurate extraction of structural planes is achieved through a multi-semantic hierarchical clustering strategy. Experimental results show that the structural planes extracted by the proposed method perform better integrity and edge adherence than that extracted by the AINet algorithm. In comparison with the results from the laser point cloud, the geological occurrence differences are less than three degrees, which proves the reliability of the results. This study widens the scope for surveying and mapping using remote sensing in engineering geological applications.

Research on the Early Warning Model for Pipelines Due to Landslide Geohazards under Multiple Influencing Factors

Because of the wide distribution of overland oil and gas pipelines, some pipelines will unavoidably pass through landslide-prone mountainous areas. Landslides may cause deformation or even damage to pipelines, affecting the normal working of the pipeline system. Therefore, it is necessary to study the multiple influence factors of pipeline deformation caused by landslides and establish a forewarning model for oil and gas pipelines buried in landslides. In the present research, the field investigation and a series of large deformation numerical simulations are conducted along four pipelines located in the southeast region of China. Results show that small soil landslides are the main types of landslides threatening the safety of pipelines, whose deformation degree mainly depends on the scale of the landslides and the location of the pipelines in the landslides. Through the investigation, the scale of landslides is the main factor determining the deformation of pipelines induced by landslides. Considering the variation of the scale of landslides, with the increase of the angles, thicknesses, and lengths of the landslides, the pipeline deformation keeps increasing. When crossing the landslides laterally, the pipeline buried in the leading edge of landslides is safer than in the tail edge. What is more, it is most dangerous when the pipeline is buried in the middle of a landslide. Considering the variation of the scale of landslides, including the longitudinal length, horizontal width, thickness, and slope of landslides, as well as the location of pipelines in the landslides, a piecewise forewarning model including those parameters was established based on the influence function for crossing pipelines in landslides. The proposed forewarning model can be used for monitoring and evaluating landslide geological disasters of pipelines and reduce the risk of pipeline landslide geological hazards in the monitored area effectively.

Using DInSAR to inventory landslide geological disaster in Bijie, Guizhou, China

Landslides are very complicated natural phenomena that create significant losses of life and assets throughout China. However, previous studies mainly focused on monitoring the development trend of known landslides in small areas, and few studies focused on the identification of new landslides. In addition, karst areas, where the vegetation is dense, the mountains are high, the slopes are steep, and the time incoherence is serious, have difficulty in tracking Differential Interferometric Synthetic Aperture Radar (DInSAR) landslides. Therefore, based on DInSAR technology, we use ALOS-2 PALSAR data to conduct continuous monitoring of existing hazards and identify new geological hazards in karst areas. The major results are as follows: 1) From June 11 to 6 August 2017, it was discovered that a hidden point of landslides occurred on the 420 m northwest mountain near the town of Zongling. It was determined that the landslide hidden point had been slipping for two consecutive years, with an average slip of 6.0 cm. From 4 September 2016 to 22 January 2017, undiscovered hidden points in the landslide account were found in Yinjiazhai. On 13 September 2016 and 22 November 2016, the discovered potential hazards in the landslide log book were the mountain hazards in southwestern Shiping village, and the deformation was 7.8 cm. 2) The DInSAR monitoring results from September to November 2016 showed that large deformations occurred in the landslide area of Shiping village. During a field visit, large cracks on the surface were found. The length of surface cracks in the southwest direction of Shiping village was 2.8 m. On 13 July 2017, Shiping collapsed as a result of the collapse of the mountainous area where the disaster occurred. The average slope of the landslide in the landslide area was approximately 65°, the height was 95 m, the length and width were 150 m and 25 m, respectively, and the thickness was 5 m. The method has shown great potential in precisely identifying some new geological hazards sites, as well as tracking and monitoring the potential hazards of geological disasters listed on the landslide account.

Sensitivity evaluation of landslide geological hazards based on Multi-source Remote Sensing Data

The landslide geological disaster caused by heavy rainfall has the characteristics of sudden and rain and slide. Due to the limitations of weather, terrain and other conditions during the disaster period, the traditional manual survey is difficult to carry out at the first time and can not meet the needs of disaster relief. Multi source remote sensing data makes up for the defects of a single sensor. Relying on its advantages of large-scale, long time series and all-weather real-time imaging, using multi-source remote sensing data technology, various evaluation elements in the survey area can be quickly extracted to investigate the sensitivity evaluation of landslide geological hazards. Landslide is one of the natural disasters that cause great losses and casualties in the world. This is a serious geological disaster second only to earthquake, which poses a great threat to people's life, property safety and economic construction. Considering the serious situation of landslide disaster and the huge losses caused by it, effectively investigating and monitoring the distribution and activities of landslide, predicting and evaluating landslide are not only the urgent requirements of the affected areas, but also the mission of landslide scientists, so as to provide a scientific basis for the prevention and management of landslide disaster. This paper will study the landslide sensitivity evaluation, which is the central problem of landslide disaster research. Landslide sensitivity, also known as landslide susceptibility, belongs to the category of landslide spatial prediction. In this paper, GIS, RS, data mining and landslide sensitivity evaluation are combined to solve the problems of low efficiency, low precision, time and labor consumption in the previous landslide sensitivity evaluation, and realize the informatization and scientization of landslide geology.

Landslide risk prediction by using GBRT algorithm: Application of artificial intelligence in disaster prevention of energy mining

Geological disasters on the slopes of open-pit mine dumps in energy extraction fall into the category of mine production process safety. For the mine safety, it is crucial to accurately predict the landslide risk of open-pit mine dumps. In order to prevent landslide geological disasters in open-pit mine dumps under the effect of heavy rainfall, this study establishes a fast and accurate landslide risk prediction model for open-pit mine dumps based on machine learning (ML). Given the actual geological conditions and rainfall of the slope of an open-pit mine dump in Shaanxi Province, Geo-Studio software is used to calculate the factor of slope safetyunder different states, and the gradient boosting regression tree (GBRT) algorithm model is used to predict the factor of slope safety. The comparison with the prediction results of different algorithms shows that the GBRT model has the highest prediction accuracy; meanwhile, the GBRT model predicts the factor of safety (FOS=1.283) for the bench slope of the dumps under the rainfall intensity (q=87 mm/d) of the "20-year rainstorm recurrence period", and its error is smaller than that calculated by the numerical simulation analysis (FOS=1.289). Therefore, the GBRT model has better applicability in predicting the safety factors of open-pit mine dumps slope under the effect of heavy rainfall, which is of great significance to realize the early warning of landslide risk in open-pit mine dumps.

Research on the Exploration and Prevention of Landslide Geological Disasters

Research on the Exploration and Prevention of Landslide Geological Disasters

STUDY ON THE CORRELATION BETWEEN EMERGENCY MONITORING AND GUIDANCE OF POST DISASTER LANDSLIDE AND POST DISASTER ANXIETY BASED ON REMOTE GROUND LASER SCANNING

BackgroundWith the rapid development of China's national economy, people pay more and more attention to disaster prevention and reduction and the impact of landslide disasters. Before the construction of various projects, disaster investigation, assessment and prediction shall be carried out to avoid in advance as much as possible, or necessary preventive measures shall be taken to prevent and reduce the occurrence of disasters as much as possible. The internal causes and treatment of landslide are studied from the aspects of point-to-point, line surface comprehensive prediction and disaster prevention and control. At the same time, the landslide damaged by high-speed and long-distance is also deeply studied. In particular, the prediction of landslide occurrence time has made a breakthrough, forming a theoretical system from treatment to prevention. However, the landslide geological disaster directly endangers the production and life of the people in the disaster area and causes psychological trauma. Accurate monitoring of micro deformation before landslide is of great significance to landslide early warning system. When using ground laser scanning (TLS) technology for landslide monitoring, the traditional point cloud data processing results, that is, comparing the changes of landslides between different surfaces, have a great deviation from the actual monitoring values.Research Objects and MethodsThis paper proposes a method to compare landslide changes by extracting the surface feature points of the monitoring object, and designs two verification experiments, namely Experiment 1 carton tilt change experiment under different design angles and Experiment 2 landslide physical model experiment. Then, in two experiments, this method is used to extract feature points with the same name. Finally, the change of inclination and the settlement of landslide are calculated. In order to test the impact of emotional behavior, this study selected subjects stimulated after the disaster, and selected 60 subjects, including 30 men and 30 women. We used the anxiety scale designed by Kim as the measurement scale, and evaluated the scale by Likert scoring method. Pearson correlation, standard deviation and statistical significance were used to illustrate the correlation. Independent sample t-test was used to verify the difference between high anxiety group and low anxiety group. According to the statistical value of anxiety, participants were divided into three groups: low, medium and high anxiety groups. The questionnaire includes two kinds of anxiety, namely debilitating anxiety and promoting anxiety. The relationship between anxiety and self-efficacy of subjects with different majors and genders was investigated by t-test, univariate, multivariate analysis of variance and regression analysis.ResultsUsing the method proposed in this paper, the change of carton inclination in Experiment 1 and the settlement of landslide surface in different periods in Experiment 2 are obtained, and the error of the results is analyzed to verify the feasibility of the method proposed in this paper. Test participants can effectively alleviate psychological stress and prevent depression. The data showed that the DASS-21 comprehensive score of subjects participating in the test was 1.7 times that of subjects not participating in the test. In contrast, without participation, they are more likely to show depression, anxiety and stress, and tend to be passive in simple coping styles. The improved method can effectively alleviate the psychological pressure of employees and improve the ability to actively deal with pressure. Through the correlation analysis of emotion regulation ability, the data results show that structural equation model is used to investigate the mediating role of core self-evaluation and emotion regulation self-efficacy between stressful life events and life satisfaction. The maximum likelihood estimation method is used to estimate the intermediary effect. The results show that the fitting indexes of this model are χ 2=314.015, df=218, χ 2/df=1.44, CFI=0.958, TLI=0.94, RMSEA=0.043. All paths in the model are significant. According to the effect decomposition principle of path analysis, the total effect from stressful life events to life satisfaction is equal to direct effect plus indirect effect. The total indirect effect in this study is equal to the sum of three specific mediating effects, which is -0.299. The proportion of indirect effect in the total effect is -0.299 / (-0.469) + (-0.299) = 0.389, that is, 38.9% of the effect of stressful life events on life satisfaction is through core self-evaluation and emotion regulation self-efficacy.ConclusionUsing the method proposed in this paper, the visual expression of the internal cause of landslide is realized. This method can calculate the displacement and inclination of key parts in different periods, and obtain the stable state of the monitoring object through comparative analysis. It is consistent with the traditional analysis method, but it improves the efficiency and accuracy, provides reliable data support for the subsequent landslide prediction, and avoids people's property loss and psychological trauma to the greatest extent.AcknowledgementThis work was financially supported by the Research Foundation of Education Bureau of Jiangxi Province, China (No. GJJ190944) and the National Natural Science Foundation of China Regional Science Fund Project (42162023).

Retraction Note to: Monitoring and early warning of loess landslide based on distributed environment and effectiveness calculation of physical training

Loess landslide has become a major geological hazard due to its high distribution, high occurrence frequency, and high loss. More than 1800 landslides occur every year in China, causing an average of more than 400 deaths every year, and the direct economic loss is as high as 4.6 billion yuan. This is a serious threat to the lives and property safety of the people in the disaster area, and caused huge losses to the country. China’s “one belt, one road” like a cooperative project has been like a raging fire in recent years, and the new mode of economic development and regional cooperation has been opened. However, one belt, one road, is seriously threatened by landslides. From 1900 to 2015 one belt, one road, along the more than 330 landslides, caused more than 20 thousand deaths. Therefore, this document collects the basic x-di loess data, uses ArcGIS software and distributed environment network, analyzes the spatial and temporal distribution characteristics of landslides, obtains the factors affecting landslides, and develops the loess landslide monitoring and early warning system. Then this paper studies the sports training, as an important part of college physical education curriculum, should carry out the corresponding reform to play a positive role and effectiveness of sports training. Some colleges and universities lack the scientific management of sports teams in the process of sports training. The overall quality and teaching ability of teachers are not high. We must cultivate students’ interest and further strengthen the reform of sports teaching. It is necessary to strengthen the psychological training of teachers and students, improve the training methods and standard training, in order to improve the effect of sports training. In this paper, through the study of loess landslide geological disasters, the monitoring, and early warning system is developed, which is applied to sports training to make sports training more effective.

Disaster control and effect evaluation of high and steep slope based on the landslide mass chain disaster model

According to the investigation on the distribution of surface geological hazards in Maoping Mine, Yunnan Province, the stability calculation and discrete element numerical simulation of Meitanyakou landslide were carried out. The result shows that under the action of earthquake and rainfall, the Meitanyaku landslide has a chain disaster mode of high source start-up, acceleration slippage on the steep slope, gentle slope debris flow scattering and accumulation, and block rolling and throwing. Based on this, the reinforcement and treatment scheme in the landslide source area was formulated: the surface soil and vegetation in the gentle slope area were used to block the landslide, the passive protective net was installed at the bottom, and the reinforcement and treatment facilities were monitored. Meanwhile, a complete set of advanced and reliable slope online monitoring and early warning systems was established. After treatment, there was no unstable landslide, and the slope and its engineering structures in the monitoring area were stable, which shows that the chain disaster mode of landslide geological disaster and the complementary scheme of prevention and control countermeasures are effective.

RETRACTED ARTICLE: Monitoring and early warning of loess landslide based on distributed environment and effectiveness calculation of physical training

Loess landslide has become a major geological hazard due to its high distribution, high occurrence frequency, and high loss. More than 1800 landslides occur every year in China, causing an average of more than 400 deaths every year, and the direct economic loss is as high as 4.6 billion yuan. This is a serious threat to the lives and property safety of the people in the disaster area, and caused huge losses to the country. China’s “one belt, one road” like a cooperative project has been like a raging fire in recent years, and the new mode of economic development and regional cooperation has been opened. However, one belt, one road, is seriously threatened by landslides. From 1900 to 2015 one belt, one road, along the more than 330 landslides, caused more than 20 thousand deaths. Therefore, this document collects the basic x-di loess data, uses ArcGIS software and distributed environment network, analyzes the spatial and temporal distribution characteristics of landslides, obtains the factors affecting landslides, and develops the loess landslide monitoring and early warning system. Then this paper studies the sports training, as an important part of college physical education curriculum, should carry out the corresponding reform to play a positive role and effectiveness of sports training. Some colleges and universities lack the scientific management of sports teams in the process of sports training. The overall quality and teaching ability of teachers are not high. We must cultivate students’ interest and further strengthen the reform of sports teaching. It is necessary to strengthen the psychological training of teachers and students, improve the training methods and standard training, in order to improve the effect of sports training. In this paper, through the study of loess landslide geological disasters, the monitoring, and early warning system is developed, which is applied to sports training to make sports training more effective.

Prediction method of landslide geological hazard based on AdaBoost model

Aiming at the problem of landslide geological disaster prediction, based on the analysis of geological structure and historical disaster data of a county in southwest Shaanxi, this paper puts forward a landslide geological disaster prediction model based on AdaBoost. Firstly, the data of landslide geological disasters are analyzed, and the main factors of landslide geological disasters are determined by principal component analysis and Spearman grade correlation coefficient method. Then, the main factors are used as feature data to train the model, and grid search is used to optimize the super parameters in the model. Finally, experiments on real data sets show that the prediction accuracy of AdaBoost model is 1.5% higher than the best prediction results of decision tree, logistic regression, support vector machine and k-nearest neighbor method, and AUC is 0.24% higher. It further verifies the effectiveness and feasibility of the model, which can provide a scientific basis for the prediction of landslide geological disasters.

Disaster characteristics and cause analysis of railroad slope in complicated and dangerous mountainous areas

With the vigorous development of China’s railroad industry, increased railroad construction has extended to complex and dangerous mountainous areas. During the construction and operation of railroads in this area, various geological disasters are inevitable, bringing significant difficulties to railroad construction and significant threats to personal safety. This study focuses on the typical railroad passing through dense and dangerous mountainous areas as the research object. Through on-site investigations, the characteristics of slope disasters along the railroad were analyzed following the laws of disaster distribution and characteristics of disaster development. The study analyzes the causes of slope disasters by investigating internal and external factors. The research results show that the geological disasters along the railroad can be divided into landslides and collapses, of which there are 8 landslide disasters and 15 collapse catastrophes. Landslides are characterized as creeping and fretting stages according to their development characteristics. Collapses can be divided into four types of dangerous rock masses according to the scale of disaster distribution. Many factors influence the development of landslide geological disasters, and the development of geological disasters is the result of the combined action of multiple influencing factors. By analyzing the slope disaster along the railroad, this study provides an effective reference basis for constructing complex mountain railroads in the future.