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.

Application of Kalman Filter Method in the Forecast of Temperature in Nanchang

A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.

A Nonmonotone Filter Method for Minimax Problems

In this paper, we propose a modified trust-region filter method algorithm for Minimax problems, which based on the framework of SQP-filter method and associated with the technique of nonmonotone method. We use the SQP subproblem to acquire an attempt step, and use the filter to weigh the effect of the attempt step so as to avoid using penalty function. The algorithm uses the Lagrange function as a merit function and the nonmonotone filter to improve the effect of the algorithm. Under some mild conditions, we prove the global convergence.

Study of nonlinear filter methods: particle filter

Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.

Filtering-efficiency measurement of Chinese-made filter 1by double-filter method

Filtering efficiency of the Chinese-made filter 1 has been measured by the double-filter method with only one set of measurement equipment. The β counts of the two filters are measured in turn for every 3 minutes, from which the decay constants are extracted to be 0.0131 min-1 and 0.0129 min-1 for the first and second filter respectively. After correcting the β counts of the second filter, the filtering efficiency of the filter is obtained to be (87.4±0.7)% in average.

Application of the N + 2 Transversal Network Method to the Study of a Coupled Resonator Filter

This paper presents a new approach to synthesize admittance function polynomials and coupling matrices for coupled resonator filters. The N + 2 transversal network method is applied to study a coupled resonator filter. This method allowed us to determine the polynomials of the reflection and transmission coefficients. A study is made for a 4 poles filter with 2 transmission zeros between the N + 2 transversal network method and the one found in the literature. A MATLAB code was designed for the numerical simulation of these coefficients for the 6, 8, and 10 pole filter with 4 transmission zeros.

基于改进Filter和随机森林的应急物资供应商评价方法研究

针对应急物资供应商评价时因主观因素影响较大、存在冗余指标等情况,导致供应商评价分类效果不佳的问题,提出基于改进Filter和随机森林的方法改善应急物资供应商评价分类预测效果的思路。利用Filter中拉普拉斯分值算法的高效计算能力进行初步特征优化,并在此过程中引入互信息法消除冗余特征,构建随机森林分类器,以期在消除主观因素影响的同时,降低冗余指标造成的不利后果。实验数据显示,该方法可以明显提升应急物资供应商评价时的分类预测效果,为应急供应链管理的快速构建和高效运作提供可靠依据。

A Filter Method for Nonlinear Semidefinite Programming with Global Convergence

In this study, a new filter algorithm is presented for solving the nonlinear semidefinite programming. This algorithm is inspired by the classical sequential quadratic programming method. Unlike the traditional filter methods, the sufficient descent is ensured by changing the step size instead of the trust region radius. Under some suitable conditions, the global convergence is obtained. In the end, some numerical experiments are given to show that the algorithm is effective.

COMPOSITE-STEP LIKE FILTER METHODS FOR EQUALITY CONSTRAINT PROBLEMS

In a composite-step approach, a step Sk is computed as the sum of two components Uk and hk. The normal component Vk, which is called the vertical step, aims to improve the linearized feasibility, while the tangential component hk, which is also called horizontal step, concentrates on reducing a model of the merit functions. As a filter method, it reduces both the infeasibility and the objective function. This is the same property of these two methods. In this paper, one concerns the composite-step like filter approach. That is, a step is tangential component hk if the infeasibility is reduced. Or else, Sk is a composite step composed of normal component Uk, and tangential component hk.

A NONMONOTONE LINE SEARCH FILTER METHOD WITH REDUCED HESSIAN UPDATING FOR NONLINEAR OPTIMIZATION

This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained optimization.In order to deal with large scale problems,a reduced Hessian matrix is approximated by BFGS updates.The new method assures global convergence without using a merit function.By Lagrangian function in the filter and nonmonotone scheme,the authors prove that the method can overcome Maratos effect without using second order correction step so that the locally superlinear convergence is achieved.The primary numerical experiments are reported to show effectiveness of the proposed algorithm.

A FILTER-TRUST-REGION METHOD FOR LC^1 UNCONSTRAINED OPTIMIZATION AND ITS GLOBAL CONVERGENCE

In this paper we present a filter-trust-region algorithm for solving LC1 unconstrained optimization problems which uses the second Dini upper directional derivative. We establish the global convergence of the algorithm under reasonable assumptions.

A tracking filter method of active sonar subject to unknown target maneuver

A tracking filter algorithm based on the maneuvering detection delay is presented in order to solve the fuzzy problem of target maneuver decision introduced by the measure？ment errors of active sonar. When the maneuvering detection is unclear, two target moving hypotheses, the uniform and the maneuver, derived from the method of multiple hypothesis tracking, are generated to delay the final decision time. Then the hypothesis test statistics is constructed by using the residual sequence. The active sonar？s tracking ability of unknown prior information targets is improved due to the modified sequential probability ratio test and the integration of the advantages of strong tracking filter and the Kalman filter. Simulation results show that the algorithm is able to not only track the uniform targets accurately, but also track the maneuvering targets steadily. The effectiveness of the algorithm for real underwater acoustic targets is further verified by the sea trial data processing results.

Software alignment of the BESⅢ main drift chamber using the Kalman Filter method

Software alignment is quite important for a tracking detector to reach its ultimate position accuracy and momentum resolution. We developed a new alignment algorithm for the BESⅢ Main Drift Chamber using the Kalman Filter method. Two different types of data which are helix tracks and straight tracks are used to test this algorithm, and the results show that the design and implementation is successful.

EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.

Hydraulic model with roughness coefficient updating method based on Kalman filter for channel flood forecast

A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning＇s roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning＇s roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning＇s roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.

Filtering Function Method for the Cauchy Problem of a Semi-Linear Elliptic Equation

A Cauchy problem for the semi-linear elliptic equation is investigated. We use a filtering function method to define a regularization solution for this ill-posed problem. The existence, uniqueness and stability of the regularization solution are proven;a convergence estimate of H?lder type for the regularization method is obtained under the a-priori bound assumption for the exact solution. An iterative scheme is proposed to calculate the regularization solution;some numerical results show that this method works well.

ixed Cadzow filtering method in fractional Fourier domain

Conventional frequency domain method used in random noise attenuation singular value decomposition （SVD） filtering processing causes bending event damage. To mitigate this problem, we present a mixed Cadzow filtering method based on fractional Fourier transform to suppress random noise in 3D seismic data. First, the seismic data is transformed to the time-frequency plane via the fractional Fourier transform. Second, based on the Eigenimage filtering method and Cadzow filtering method, the mixed high-dimensional Hankel matrix is built; then, SVD is performed. Finally, random noise is eliminated effectively by reducing the rank of the matrix. The theoretical model and real applications of the mixed filtering method in a region of Sichuan show that our method can not only suppress noise effectively but also preserve the frequency and phase of effective signals quite well and significantly improve the signal-to-noise ratio of 3D post-stack seismic data.

The background calculation of the filter-fluorescer method

For the measurement of X-ray energy spectrum with filter-fluorescer method,the high energy tail at the rearward of response function is the main source of background.There are,traditionally,two techniques for measuring the background contributions:the same element Z for the fluorescer and filter,or the same element Z?1 for them.Using the formula of fluorescence intensity,and the elemental combinations of cobalt and iron,the backgrounds which the two techniques provide and the real one for measuring black-body radiation at equilibrium temperature of 7×106 K,were calculated.The results show that the Co-Co combination for the filter-fluorescer spectrometer can achieve better effect.A semi-quantitative analysis for the calculation results is discussed.

Alteration of Taste Acuity in Pediatric Cancer Patients after Treatment Completion: A Cross-Sectional Study Using a Filter-Paper Disc Method

Taste acuity of adult patients undergoing cancer treatment has been well investigated;however, studies of taste acuity after completion of cancer treatment are limited, particularly in children. This study aimed to assess taste acuity in pediatric cancer patients after treatment completion. Seventy-three patients who had completed cancer treatment (median age, 13 years;range, 7 - 18 years) and had not received any further treatment for at least 6 months were enrolled. Eighty-one healthy children (median age, 10 years;range, 8 - 19 years) served as controls. We determined the thresholds for four tastes (sweet, salty, sour, and bitter) using the filter-paper disc method. There was no significant difference in the thresholds of taste acuity for the four test solutions between the patient and control groups. The duration since treatment completion (<5 years vs. ≥5 years) had no significant impact on taste acuity for the four test solutions. The threshold for tasting salt was significantly higher in the group that had received chemotherapy + radiation and/or hematopoietic stem cell transplantation than that in the group that had received chemotherapy-only. Our results indicated that taste acuity after treatment completion in pediatric cancer patients was the same as that in healthy children. However, some treatment modalities were correlated with an impaired ability to taste salt. Gustatory test results should be considered while deciding nutritional support modalities after treatment completion in pediatric cancer patients.

非定常Stokes/Darcy模型一种新的time filter算法的分析

首先,在非定常Stokes/Darcy模型的线性多步法的一阶θ-格式的基础上,该文结合time filter算法在几乎不增加计算量的情况下有效地将线性多步法的收敛阶由一阶提高到二阶,从而提出一种新的高效数值算法.其次,该文分别对耦合和解耦的线性多步法加time filter算法的稳定性和误差估计进行了理论分析.最后,数值实验进一步展示了耦合和解耦算法的有效性,收敛性和高效性.