GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

A Bivariate Software Reliability Model with Change-Point and Its Applications

Testing-time when a change of a stochastic characteristic of the software failure-occurrence time or software failure-occurrence time-interval is observed is called change-point. It is said that effect of the change-point on the software reliability growth process influences on accuracy for software reliability assessment based on a software reliability growth model (SRGM). We propose an SRGM with the effect of the change-point based on a bivariate SRGM, in which the software reliability growth process is assumed to depend on the testing-time and testing-effort factors simultaneously, for accurate software reliability assessment. And we discuss an optimal software release problem for deriving optimal testing-effort expenditures based on our model. Further, we show numerical examples of software reliability assessment based on our bivariate SRGM and estimation of optimal testing-effort expenditures by using actual data.

The Cox-Aalen Models as Framework for Construction of Bivariate Probability Distributions, Universal Representation

Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict ourselves to model positive stochastic dependences only with the general assumption that the underlying two marginal random variables are centered on the set of nonnegative real values. With only these assumptions we obtain nice general characterization of bivariate probability distributions that may play similar role as the copula methodology. Examples of reliability and biomedical applications are given.

Zero Truncated Bivariate Poisson Model: Marginal-Conditional Modeling Approach with an Application to Traffic Accident Data

A new covariate dependent zero-truncated bivariate Poisson model is proposed in this paper employing generalized linear model. A marginal-conditional approach is used to show the bivariate model. The proposed model with estimation procedure and tests for goodness-of-fit and under (or over) dispersion are shown and applied to road safety data. Two correlated outcome variables considered in this study are number of cars involved in an accident and number of casualties for given number of cars.

Median Unbiased Estimation of Bivariate Predictive Regression Models with Heavy-tailed or Heteroscedastic Errors

In this paper, we consider median unbiased estimation of bivariate predictive regression models with non-normal, heavy-tailed or heteroscedastic errors. We construct confidence intervals and median unbiased estimator for the parameter of interest. We show that the proposed estimator has better predictive potential than the usual least squares estimator via simulation. An empirical application to finance is given. And a possible extension of the estimation procedure to cointegration models is also described.

基于Bivariate Probit模型的土地流转影响因素分析——以江西省为例

在2011年江西省农户调研数据的基础上,利用Bivariate Probit模型分析了影响农户土地流转行为的因素及土地转入转出行为之间的联立关系。计量结果显示:农户土地转入和转出行为之间存在替代效应;家庭女男比例、人均承包地面积、户主年龄及受教育年限、拥有的农业资产价值、外出务工及土地调整等是影响农户土地流转行为的重要因素。

Reliability of a Multicomponent Stress-strength Model Based on a Bivariate Kumaraswamy Distribution with Censored Data

In this paper,we consider a system which has k statistically independent and identically distributed strength components and each component is constructed by a pair of statistically dependent elements with doubly type-II censored scheme.These elements(X1,Y1),(X2,Y2),…,(Xk,Yk)follow a bivariate Kumaraswamy distribution and each element is exposed to a common random stress T which follows a Kumaraswamy distribution.The system is regarded as operating only if at least s out of k(1≤s≤k)strength variables exceed the random stress.The multicomponent reliability of the system is given by Rs,k=P(at least s of the(Z1,…,Zk)exceed T)where Zi=min(Xi,Yi),i=1,…,k.The Bayes estimates of Rs,k have been developed by using the Markov Chain Monte Carlo methods due to the lack of explicit forms.The uniformly minimum variance unbiased and exact Bayes estimates of Rs,k are obtained analytically when the common second shape parameter is known.The asymptotic confidence interval and the highest probability density credible interval are constructed for Rs,k.The reliability estimators are compared by using the estimated risks through Monte Carlo simulations.

Statistical inference for dependence competing risks model under middle censoring

Middle censoring is an important censoring scheme,in which the actual failure data of an observation becomes unobservable if it falls into a random interval. This paper considers the statistical analysis of the dependent competing risks model by using the Marshall-Olkin bivariate Weibull(MOBW) distribution.The maximum likelihood estimations(MLEs), midpoint approximation(MPA) estimations and approximate confidence intervals(ACIs) of the unknown parameters are obtained. In addition, the Bayes approach is also considered based on the Gamma-Dirichlet prior of the scale parameters, with the given shape parameter.The acceptance-rejection sampling method is used to obtain the Bayes estimations and construct credible intervals(CIs). Finally,two numerical examples are used to show the performance of the proposed methods.

Investigation of Probability Generating Function in an Interdependent <i>M/M/</i>1:(∞;GD) Queueing Model with Controllable Arrival Rates Using Rouche’s Theorem

Present paper deals a M/M/1:(∞;GD) queueing model with interdependent controllable arrival and service rates where- in customers arrive in the system according to poisson distribution with two different arrivals rates-slower and faster as per controllable arrival policy. Keeping in view the general trend of interdependent arrival and service processes, it is presumed that random variables of arrival and service processes follow a bivariate poisson distribution and the server provides his services under general discipline of service rule in an infinitely large waiting space. In this paper, our central attention is to explore the probability generating functions using Rouche’s theorem in both cases of slower and faster arrival rates of the queueing model taken into consideration;which may be helpful for mathematicians and researchers for establishing significant performance measures of the model. Moreover, for the purpose of high-lighting the application aspect of our investigated result, very recently Maurya [1] has derived successfully the expected busy periods of the server in both cases of slower and faster arrival rates, which have also been presented by the end of this paper.

Return threshold model analysis of two stock markets： Evidence study of Italy and Germany＇s stock returns

This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.

东北黑土区典型县域耕地质量对耕地水田化的空间响应

[目的]耕地水田化过程对耕地质量具有至关重要的影响,明确东北黑土区耕地质量对耕地水田化的空间响应,是提升黑土耕地质量实现耕地可持续利用的重要前提。[方法]构建LESA耕地质量评价体系,采用CRITIC模型、双变量局部空间自相关模型,揭示耕地质量和耕地利用变化之间的空间响应关系。[结果](1)研究区耕地水田化现象剧烈,呈现出东部最强、西部次之、中部最弱的时空分异格局;(2)研究区耕地质量变化显著,耕地质量下降比例低于质量提升比例,表现出中部下降最高、西部下降次高、东部下降最低的时空格局;(3)研究区耕地质量对水田化的响应具有双重效应,负向响应更为敏感,整体呈现出东部耕地质量响应程度高于其他区域的空间格局。[结论]研究区耕地水田化对耕地质量具有正、负两种效应,依据效应方向进行耕地利用结构调整,是保护黑土耕地的重要途径。对于具有正向效应的区域应适度推进旱改水项目,而针对具有负向效应的区域应停止旱改水工程,积极进行水田休耕,并可适当地将其恢复为旱地。

基于二元相关退化的动车组车载变压器油纸绝缘剩余寿命预测

针对动车组车载变压器油纸绝缘剩余寿命预测中单性能退化指标难以全面反映油纸绝缘退化过程的问题,考虑车载变压器油纸绝缘退化的个体差异性及两性能指标间的相关关系,提出了基于Copula函数的两性能指标相关退化的油纸绝缘剩余寿命预测方法:采用具有随机效应的维纳过程建立油纸绝缘的两性能指标相关退化模型,基于赤池信息准则(Akaike Information Criterion, AIC)选择拟合效果更优的Copula函数来描述两性能指标间的相关关系,采用最大似然估计法估计初始时刻的模型参数,基于序列贝叶斯更新方法在线更新退化模型中的漂移系数,以实现油纸绝缘剩余寿命的在线预测。最后以加速热老化试验下油纸绝缘的聚合度和抗拉强度的退化数据进行实例验证。结果表明,两性能指标相关退化模型比单性能指标退化模型的剩余寿命预测值与实际值之间的平均绝对误差更小,预测的准确性更高,且随着模型参数不断更新,剩余寿命的预测值与实际值间的绝对误差在不断减小,预测结果的准确性在不断提升。

Influence of Human Activity Intensity on Habitat Quality in Hainan Tropical Rainforest National Park,China

Due to long-term human activity interference,the Hainan Tropical Rainforest National Park(HTRNP)of China has experienced ecological problems such as habitat fragmentation and biodiversity loss,and with the expanding scope and intensity of human activity impact,the regional ecological security is facing serious challenges.A scientific assessment of the interrelationship between human activity intensity and habitat quality in the HTRNP is a prerequisite for achieving effective management of ecological disturbances caused by human activities and can also provide scientific strategies for the sustainable development of the region.Based on the land use change data in 2000,2010,and 2020,the spatial and temporal variations and the relationship between habitat quality(HQ)and human activity intensity(HAI)in the HTRNP were explored using the integrated valuation of ecosystem services and trade-offs(InVEST)model.System dynamics and land use simulation models were also combined to conduct multi-scenario simulations of their relationships.The results showed that during 2000–2020,the habitat quality of the HTRNP improved,the intensity of human activities decreased each year,and there was a negative correlation between the two.Second,the system dynamic model could be well coupled with the land use simulation model by combining socio-economic and natural factors.The simulation scenarios of the coupling model showed that the harmonious development(HD)scenario is effective in curbing the increasing trend of human activity intensity and decreasing trend of habitat quality,with a weaker trade-off between the two compared with the baseline development(BD)and investment priority oriented(IPO)scenarios.To maintain the authenticity and integrity of the HTRNP,effective measures such as ecological corridor construction,ecological restoration,and the implementation of ecological compensation policies need to be strengthened.

生境质量对气候变化与人类活动的空间响应——以青藏高原为例

[目的]探究青藏高原生境质量的动态变化,为保障青藏高原生态安全、保护生物多样性和筑牢生态屏障提供参考。[方法]基于1985年、1995年、2005年、2015年和2022年5期土地利用数据,借助InVEST模型,定量评估1985-2022年青藏高原生境质量状况并对其时空格局进行分析,最后利用双变量空间自相关探讨生境质量对气候变化与人类活动的空间响应。[结果](1)1985-2022年青藏高原生境指数增加0.0529,高等和较高生境面积比例共增加5.51%,生境质量水平总体提升但仍处于较低等级。(2)1985-2022年青藏高原48.14%的区域生境指数增加,但生境等级总体空间分布变化不大,各等级区由外向内呈嵌套式等级增加。(3)1985-2022年青藏高原生境质量等级转移类型以等级不变为主,但等级转化的剧烈程度不断增强。(4)1985-2022年青藏高原生境质量对气候变化与人类活动存在较为显著的空间相关关系,年平均气温对生境质量存在显著的空间负相关,年降水量和人类活动强度对生境质量存在显著的空间正相关。[结论]研究结果揭示青藏高原生境质量时空演变特征,明确气候变化和人类活动对生境质量具有深刻的影响,未来应加强底线约束和人类活动引导。

高血压人群年龄及体重指数对癌症发病共同作用的回顾性队列研究

目的在上海市闵行区高血压人群中,探究年龄和体重指数(body mass index,BMI)对癌症发生的共同作用。方法研究对象为2007—2015年进入上海市闵行区电子健康信息系统的未患癌症的212394名高血压患者。将年龄、BMI以平滑函数形式纳入广义加性Cox比例风险模型,用双变量响应模型构建曲面图使结果可视化,全面分析两者对癌症发生的联合效应。结果截至2018年12月31日,累计22141名高血压患者新发癌症。年龄与癌症发生风险整体呈线性趋势,而BMI与癌症发生风险整体呈“U”型,BMI在26 kg/m^(2)左右时癌症发生风险最低。不同BMI下,随着年龄增大,癌症发生风险均增加;不同年龄下,BMI与癌症发生风险的关联不同:青年人群(20~44岁)中BMI与癌症发生风险无明显关联,中老年人群(≥45岁)中BMI与癌症发生风险呈“U”型关联,BMI在26 kg/m^(2)左右时癌症发生风险最低。结论建议高血压人群控制BMI在合理范围内,特别是中老年人群,以减少癌症发生。

旅游城镇化格局与生态系统服务的时空关联分析——以武夷山市为例

以典型旅游城市武夷山市为例,在分析城镇化格局、碳存储服务与土壤保持服务时空变化格局的基础上,采用双变量空间自相关等方法探索武夷山市旅游城镇化格局与生态系统服务之间的时空关联。研究表明:(1)2000—2010年武夷山市旅游城镇化进程发展迅速,陆地表层人类活动由3.79%升至5.05%,2010—2020年城镇化进程放缓,2020年陆地表层人类活动仅为5.33%;空间分异明显。(2)2000—2020年,碳存储服务呈下降趋势,土壤保持服务价值总量呈上升趋势,由2000年的26.38106 t升至2020年的45.26106 t。(3)陆地表层人类活动与碳存储服务之间存在负相关关系,而陆地表层人类活动与土壤保持服务存在正相关关系。旅游城镇化格局与碳存储和土壤保持2种生态系统服务之间呈现显著的时空关联特征。

Bivariate Constant-Stress Accelerated Degradation Model and Inference Based on the Inverse Gaussian Process

Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliability of these products. This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian(IG) process. We assume that the product considered has two QCs and each of the QCs is governed by an IG process. The relationship between the QCs is described by a Frank copula function. We also assume that the stress on the products affects not only the parameters of the IG processes, but also the parameter of the Frank copula function. The Bayesian MCMC method is developed to calculate the maximum likelihood estimators(MLE) of the model parameters. The reliability function and the mean-time-to-failure(MTTF) are estimated through the calculation of the posterior samples. Finally, a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.

积极与消极情绪关系的理论及研究

积极与消极情绪的关系一直是情绪领域中最引人注目的焦点之一。该文介绍了探讨积极与消极情绪关系的模型及相关的证据,包括以下4个方面的内容:(1)情绪的双极模型及相关证据;(2)情绪的双变量模型及相关证据;(3)情绪的混合模型及相关证据;(4)解决各种模型争论的新视角:条件决定论。文章最后分析了该领域研究的不足,并提出了未来研究的方向。

SARS对中国入境旅游人数影响的研究

今年爆发的SARS对中国旅游业冲击很大。本文利用双变量ARIMA模型 ,结合突发事件后人的心理发展变化曲线研究本次突发事件对入境旅游人数的影响。根据同类事件旅游心理恢复期的研究 ,结合中国实际情况 ,笔者讨论了恢复期分别为 1 2、 1 8、 2 4个月时 ,SARS对入境旅游人数的影响。当恢复期为 1 2个月时 ,入境旅游同比增长率平均降幅为 1 7 96 % ,入境旅游人数共减少 2 384 93万人次 ;当恢复期为 1 8个月时 ,入境旅游同比增长率平均降幅为 1 7 0 7% ,入境旅游人数共减少 31 1 7 0 3万人次 ;当恢复期为 2 4个月时 ,入境旅游同比增长率平均降幅为 1 6 5 5 % ,入境旅游人数共减少 385 2 33万人次。

两变量水文频率分布模型研究述评

水文变量多特征属性的频率分析,以及各种水文事件的遭遇及联合概率分布问题需要采用多变量概率分布模型解决。总结了当前应用最广泛的几种两变量概率分布模型,对各种模型的适用性和局限性做了详细分析,并介绍了一种新的两变量概率模型——Copula函数。现有模型大都基于变量之间的线性相关关系而建立,对于非线性、非对称的随机变量难以很好地描述;大部分模型假定各变量服从相同的边际分布或对变量间的相关性有严格的限定,从而限制了其应用。Copula函数所构造的两变量概率分布模型克服了现有模型的不足,它具有任意的边际分布,可以描述变量间非线性、非对称的相关关系。作为一种用于构造灵活的多变量联合分布的工具,Copula函数在水科学领域具有广阔的应用前景。