Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Factors affecting land dissection density in geomorphological regions of China

Land dissection density(LDD)provides morphological evidence regarding prior intense soil erosion and quantifies the distribution of land dissections.A comprehensive understanding of the potential factors influencing the spatial pattern and value of the LDD is vital in geological disasters,soil erosion,and other related domains.Land dissection phenomena in China affects large areas with different morphological,pedological,and climatic characteristics.Prior studies have focused on the potential factors influencing the LDD at a watershed scale.However,these results are insufficient to reflect the status quo of dissection development and its primary influencing factors on a national scale.LDD’s spatial patterns and the dominant factors at a regional scale in millions of square kilometers remain to be ascertained.This study used the geomorphon-based method and the geographical detector model to quantify the spatial pattern of LDD over China and identify the dominant factors affecting this pattern in China’s six first-order geomorphological regions(GR1~GR6).The results yield the following findings:(1)LDD in China ranges from 0~4.55 km/km^(2),which is larger in central and eastern regions than in other regions of China;(2)dominant factors and their dominant risk subcategories vary with each geomorphological region’s primary internal and external forces;(3)the influence of natural factors is more significant on the large regional scale in millions of square kilometers compared to anthropogenic factors;relief degree of land surface(RDLS)is dominant in GR1,GR2,and GR5;the slope is dominant in GR6,soil type is dominant in GR3 and GR4,and lithology plays a critical role in the dominant interactions of GR3,GR4,and GR6;(4)the interactions between factors on LDD’s spatial pattern have a more significant effect than individual factors.

The Factors of the Failure in the Treatment of Congenital Equinus Clubfoot Varus by the Ponseti Method at the Reference Health Care Center (CSREF) of Commune III of Bamako

Clubfoot malformation is the most common serious congenital anomaly affecting the foot in children. Its treatment by the Ponseti method is simple, profitable and widely used in the world. Objective: The objective of this study was to identify the factors of the failure of its treatment by the Ponseti method. Material and Method: We carried out a retrospective and descriptive study of cases of congenital equinus clubfoot varus at the Reference Health Care Center of Commune III of Bamako over 26 months from September 2020 to November 2022. Data were treated with the utmost anonymity. Result: This study was performed on 44 children seen for clubfoot: male (68%) and female (32%), with a sex ratio of 2.1. We obtained 13 cases of recidivism including 7 boys and 6 girls. We found 21 cases of unilateral and 23 bilateral;among which 9 recurrences were found against 4 in the unilateral forms. 85% of recurrences did not have good adherence to the splint and 62% did not come regularly for follow-up consultation. We obtained 33 children with idiopathic clubfeet (75%) with a recurrence of 24%, and 7 children with secondary clubfeet with 71 % recurrence. There was no recurrence in the postural type. Among the recurrences, 38.5% started treatment between 1 and 6 months, 23.1% from 0 to 1 month and 15.4% at 2 years and more. 85% of recurrences had a Pirani score between 4.5 to 6 at the start of treatment and 15% at a score of 2.5 to 4. Conclusion: The factors of the failure of the Ponseti method are mainly non-compliance with treatment, secondary clubfeet, and a high Pirani score at the start of treatment.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Estimation of quality factors by energy ratio method

The quality factor Q, which reflects the energy attenuation of seismic waves in subsurface media, is a diagnostic tool for hydrocarbon detection and reservoir characterization. In this paper, we propose a new Q extraction method based on the energy ratio before and after the wavelet attenuation, named the energy-ratio method（ERM）. The proposed method uses multipoint signal data in the time domain to estimate the wavelet energy without invoking the source wavelet spectrum, which is necessary in conventional Q extraction methods, and is applicable to any source wavelet spectrum; however, it requires high-precision seismic data. Forward zero-offset VSP modeling suggests that the ERM can be used for reliable Q inversion after nonintrinsic attenuation（geometric dispersion, refl ection, and transmission loss） compensation. The application to real zero-offset VSP data shows that the Q values extracted by the ERM and spectral ratio methods are identical, which proves the reliability of the new method.

METHOD TO CALCULATE BENDING CENTER AND STRESS INTENSITY FACTORS OF CRACKED CYLINDER UNDER SAINT_VENANT BENDING

Using the single crack solution and the regular solution of plane harmonic function, the problem of Saint_Venant bending of a cracked cylinder by a transverse force was reduced to solving two sets of integral equations and its general solution was then obtained. Based on the obtained solution, a method to calculate the bending center and the stress intensity factors of the cracked cylinger whose cross_section is not thin_walled, but of small torsion rigidity is proposed. Some numerical examples are given.

Rutting influencing factors and prediction model for asphalt pavements based on the factor analysis method

To clarify the importance of various influencing factors on asphalt pavement rutting deformation and determine a screening method of model indicators,the data of the RIOHTrack full-scale track were examined using the factor analysis method(FAM).Taking the standard test pavement structure of RIOHTrack as an example,four rutting influencing factors from different aspects were determined through statistical analysis.Furthermore,the common influencing factors among the rutting influencing factors were studied based on FAM.Results show that the common factor can well characterize accumulative ESALs,center-point deflection,and temperature,besides humidity,which indicates that these three influencing factors can have an important impact on rutting.Moreover,an empirical rutting prediction model was established based on the selected influencing factors,which proved to exhibit high prediction accuracy.These analysis results demonstrate that the FAM is an effective screening method for rutting prediction model indicators,which provides a reference for the selection of independent model indicators in other rutting prediction model research when used in other areas and is of great significance for the prediction and control of rutting distress.

SEMI-WEIGHT FUNCTION METHOD ON COMPUTATION OF STRESS INTENSITY FACTORS IN DISSIMILAR MATERIALS

Semi_weight function method is developed to solve the plane problem of two bonded dissimilar materials containing a crack along the bond. From equilibrium equation, stress and strain relationship, conditions of continuity across interface and free crack surface, the stress and displacement fields were obtained. The eigenvalue of these fields is lambda. Semi_weight functions were obtained as virtual displacement and stress fields with eigenvalue?_lambda. Integral expression of fracture parameters, K Ⅰ and K Ⅱ, were obtained from reciprocal work theorem with semi_weight functions and approximate displacement and stress values on any integral path around crack tip. The calculation results of applications show that the semi_weight function method is a simple, convenient and high precision calculation method.

Weight Determining of Factors Influencing Grain Output Based on Entropy Weight Method

This article selects 8 main factors(the number of rural employees,total power of agricultural machinery,effective irrigation area of crops,growing area of grain crops,fertilizer consumption,electricity consumption in rural areas,area affected and area covered) as the factors influencing grain output,and offers the method of determining weight of factors influencing grain output using entropy weight method.According to the relevant data in the period 1985-2005,we analyze the weight of factors influencing grain output in China by example.The results show that the electricity consumption in rural areas has the greatest impact on grain output,followed by total power of agricultural machinery,fertilizer consumption and area covered.To increase grain output,we must enhance the degree of mechanization,free people from the former process of direct cultivation,strengthen water conservancy construction,and do a good job in disaster prevention and mitigation.

Evaluation of mixed-mode stress intensity factors by extended finite element method

Extended finite element method （XFEM） implementation of the interaction integral methodology for evaluating the stress intensity factors （SIF） of the mixed-mode crack problem is presented. A discontinuous function and the near-tip asymptotic function are added to the classic finite element approximation to model the crack behavior. Two-state integral by the superposition of actual and auxiliary fields is derived to calculate the SIFs. Applications of the proposed technique to the inclined centre crack plate with inclined angle from 0° to 90° and slant edge crack plate with slant angle 45°, 67.5° and 90° are presented, and comparisons are made with closed form solutions. The results show that the proposed method is convenient, accurate and computationallv efficient.

STRESS INTENSITY FACTORS CALCULATION IN ANTI-PLANE FRACTURE PROBLEM BY ORTHOGONAL INTEGRAL EXTRACTION METHOD BASED ON FEMOL

For an anti-plane problem, the differential operator is self-adjoint and the corresponding eigenfunctions belong to the Hilbert space. The orthogonal property between eigenfunctions （or between the derivatives of eigenfunctions） of anti-plane problem is exploited. We developed for the first time two sets of radius-independent orthogonal integrals for extraction of stress intensity factors （SIFs）, so any order SIF can be extracted based on a certain known solution of displacement （an analytic result or a numerical result）. Many numerical examples based on the finite element method of lines （FEMOL） show that the present method is very powerful and efficient.

Study on the Carbon Emission Factors in Guangdong Province Based on Divisia Decomposition Method

[Objective] By decomposing and studying the relative factors of carbon emissions in Guangdong Province,the policy and suggestion on further keeping the sustainable development were put forward,which provided the reference for the carbon emission reduction in other provinces.[Method] Based on the carbon emissions formula which was put forward by Johan,three factors(the energy structure,energy efficiency and economy development) which affected the carbon emissions during 1996-2009 in Guangdong Province were studied by using Divisia decomposition method of logarithmic mean weight(LMD).[Result] The economy development was the main reason that caused the continuous significant increase of carbon emissions in Guangdong Province.The improvement of energy efficiency was the important manner for decreasing the energy consumption and the carbon emissions.The adjustment and optimization of energy consumption structure had the huge potential for reducing the carbon emissions in Guangdong Province.[Conclusion] The carbon emissions in Guangdong Province would continue to increase in the future for a long time.When formulated the development strategy in the future,it needed pay special attention to keep the accord development of economy and environment.

Influence Factors of Phosphorus Removal by Chemical Method in Sewage Treatment System

[Objective] The research aimed to study influence factors of phosphorus removal by chemical method in sewage treatment system. [ Method] In different reaction systems, removal effect of the phosphorus in sewage by dosing lime and influence of the constraint factor were stud- ied. [ Result] Lime precipitation method treating high-concentration phosphorus wastewater could not only decline cost of phosphorus removal by chemical method, but also reach better treatment effect under suitable stirring and precipitation conditions by controlling alkalinity and pH. Phosphor- us content of chemical sludge after treatment could reach 9% -12%, with higher recyclable value. E Conclusion] Lime method treating phosphorus- rich sewage was more economic than low-concentration phosphorus sewage, and had very great potential for recycling phosphorus.

A method to calculate displacement factors using SVM

In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.

Quantitative analysis on influencing factors for interface propagation-based thermal conductivity measurement method during solid-liquid transition

The recently proposed interface propagation-based method has shown its advantages in obtaining the thermal conductivity of phase change materials during solid-liquid transition over conventional techniques. However, in previous investigation, the analysis on the measurement error was qualitative and only focused on the total effects on the measurement without decoupling the influencing factors. This paper discusses the effects of influencing factors on the measurement results for the interface propagation-based method. Numerical simulations were performed to explore the influencing factors, namely model simplification, subcooling and natural convection, along with their impact on the measurement process and corresponding measurement results. The numerical solutions were provided in terms of moving curves of the solid-liquid interface and the predicted values of thermal conductivity. Results indicated that the impact of simplified model was strongly dependent on Stefan number of the melting process. The degree of subcooling would lead to underestimated values for thermal conductivity prediction. The natural convection would intensify the heat transfer rate in the liquid region, thereby overestimating the obtained results of thermal conductivity. Correlations and experimental guidelines are provided. The relative errors are limited in ±1.5%,±3%and ±2% corresponding to the impact of simplified model, subcooling and natural convection, respectively.

Exponential Time Differencing Method for a Reaction-Diffusion System with Free Boundary

For reaction-diffusion equations in irregular domains with moving boundaries,the numerical stability constraints from the reaction and diffusion terms often require very restricted time step sizes,while complex geometries may lead to difficulties in the accuracy when discretizing the high-order derivatives on grid points near the boundary.It is very challenging to design numerical methods that can efficiently and accurately handle both difficulties.Applying an implicit scheme may be able to remove the stability constraints on the time step,however,it usually requires solving a large global system of nonlinear equations for each time step,and the computational cost could be significant.Integration factor(IF)or exponential time differencing(ETD)methods are one of the popular methods for temporal partial differential equations(PDEs)among many other methods.In our paper,we couple ETD methods with an embedded boundary method to solve a system of reaction-diffusion equations with complex geometries.In particular,we rewrite all ETD schemes into a linear combination of specificФ-functions and apply one state-of-the-art algorithm to compute the matrix-vector multiplications,which offers significant computational advantages with adaptive Krylov subspaces.In addition,we extend this method by incorporating the level set method to solve the free boundary problem.The accuracy,stability,and efficiency of the developed method are demonstrated by numerical examples.

Negative Factors in Higher Vocational Colleges'English Teaching and the Methods to It

This thesis will analysis the negative factors in higher vocational colleges'English teaching and try to find effective methods to it.

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

Failure evaluation and control factor analysis of slope block instability along traffic corridor in Southeastern Tibet

The instability of slope blocks occurred frequently along traffic corridor in Southeastern Tibet(TCST),which was primarily controlled by the rock mass structures.A rapid method evaluating the control effects of rock mass structures was proposed through field statistics of the slopes and rock mass structures along TCST,which combined the stereographic projection method,modified M-JCS model,and limit equilibrium theory.The instabilities of slope blocks along TCST were then evaluated rapidly,and the different control factors of instability were analyzed.Results showed that the probabilities of toppling(5.31%),planar(16.15%),and wedge(35.37%)failure of slope blocks along TCST increased sequentially.These instability modes were respectively controlled by the anti-dip joint,the joint parallel to slope surface with a dip angle smaller than the slope angle(singlejoint),and two groups of joints inclined out of the slope(double-joints).Regarding the control effects on slope block instability,the stabilization ability of doublejoints(72.7%),anti-dip joint(67.4%),and single-joint(57.6%)decreased sequentially,resulting in different probabilities of slope block instability.Additionally,nearby regional faults significantly influenced the joints,leading to spatial heterogeneity and segmental clustering in the stabilization ability provided by joints to the slope blocks.Consequently,the stability of slope blocks gradually weakened as they approached the fault zones.This paper can provide guidance and assistance for investigating the development characteristics of rock mass structures and the stability of slope blocks.