Possibilities of land use change analysis in a mountainous rural area: a methodological approach

Decreasing population density is a current trend in the European Union, and causes a lower environmental impact on the landscape. However, besides the desirable effect on the regeneration processes of semi-natural forest ecosystems, the lack of traditional management techniques can also lead to detrimental ecological processes. In this study we investigated the land use pattern changes in a micro-region (in North-Eastern Hungary) between 1952 and 2005, based on vectorised land use data from archive aerial photos. We also evaluated the methodology of comparisons using GIS methods, fuzzy sets and landscape metrics. We found that both GIS methods and statistical analysis of landscape metrics resulted in more or less the same findings. Differences were not as relevant as was expected considering the general tendencies of the past 60 years in Hungary. The change in the annual rate of forest recovery was 0.12%; settlements extended their area by an annual rate of 3.04%, while grasslands and arable lands had a net loss in their area within the studied period (0.60% and 0.89%, respectively). The kappa index showed a smaller similarity (~60%) between these dates but the fuzzy kappa and the aggregation index, taking into account both spatial and thematic errors, gave a more reliable result (~70–80% similarity). Landscape metrics on patch and class level ensured the possibility of a detailed analysis. We arrived at a similar outcome but were able to verify all the calculations through statistical tests. With this approach we were able to reveal significant (p < 0.05) changes; however, effect sizes did not show large magnitudes. Comparing the methods of revealing landscape change, the approach of landscape metrics was the most effective approach, as it was independent of spatial errors and ensuring a multiple way of interpretation.     

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

金沙江白格滑坡残留体稳定性分析与防治对策

金沙江白格滑坡在经历了2018年10月和11月的2次滑动堵江后,在其后缘仍存在K1、K2和K3等3处规模较大的残留崩滑体,并有再次失稳堵江的可能。目前关于残留体稳定性的研究还存在较大争议,多从影响因素及成因机制等方面进行定性分析,其结果依赖于评价者的经验,差异较大。在野外调查的基础上,针对白格滑坡所在区工程地质条件及其残留体变形特征,选取坡高、坡度、坡面形态、临空面、地下水出露、裂缝发育和变形等7个因素作为评价因子,基于模糊综合评判模型,运用层次分析法对白格滑坡残留体进行分区稳定性评价,并提出相应的防治对策。评价结果表明:K1与K2残留体稳定性较差;K3残留体基本稳定,与实际调查状况较吻合。研究结果可为同类型滑坡的评价与防治提供参考。     

基于模糊熵的GLLE熵阈值分割方法

图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE（Gray Level and Local Entropy）二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.     

THE THEORY OF RESPONSE ANALYSIS OF FUZZY STOCHASTIC DYNAMICAL SYSTEMS WITH A SINGLE DEGREE OF FREEDOM

Most real-life structural/mechanical systems have complex geometrical and material properties and operate under complex fuzzy environmental conditions. These systems are certainly subjected to fuzzy random excitations induced by the environment. For an analytical treatment of such a system subjected to fuzzy random excitations, it becomes necessary to establish the general theory of dynamic response of a system to fuzzy random excitations. In this paper, the theory of response, fuzzy mean response and fuzzy covariance response of a single-degree-of-freedom (sdf) system to fuzzy random excitations in the time domain and frequency domain is put forward. The theory of response analysis of an sdf system to both stationary and non-stationary fuzzy random excitations in the time domain and frequency domain is established. Two examples are considered in order to demonstrate the rationality and validity of the theory, and the models of stationary filtered white noise and non-stationary filtered white noise fuzzy stochastic processes of the earthquake ground motion are set up. Methods of analysis for fuzzy random seismic response of sdf systems are put forward using the principles of response analysis of an sdf fuzzy random dynamic system.     

基于模糊预测对象的证据权方法及其在土地沙漠化评价中的应用

为了使在地理信息系统中被广泛用于点事件预测的证据权方法能对面事件进行评价和预测, 提出了一种新的基于模糊训练层的证据权方法.它是一种更广泛的证据权方法, 与普通证据权方法所不同的是, 它的训练层是模糊集合, 其取值是它的隶属度.通过适当的变换也可以把点训练层转换为模糊集合.因此, 该方法可以对面事件、点事件和线事件进行评价和预测.该方法可以处理训练层和证据层均为模糊集合的情况, 被称为双重模糊证据权方法.作为该方法的一个应用实例, 本文介绍毛乌素沙漠边缘的晋陕蒙地区土地沙漠化评价的应用实例.      

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

大深度载人潜水器低速大漂角模糊滑模航向控制研究

通过模型试验测量大深度载人潜水器低速大漂角运动时所受到的非线性水动力。基于一种新的模糊滑模控制策略,为潜水器设计了鲁棒航向控制器。在不同的漂角子区间内分别设计局部镇定的滑模控制器,然后通过Takagi-Sugeno模糊推理系统将它们光滑连接,得到模糊滑模控制。仿真计算结果充分显示了该控制策略的有效性。     

基于模糊神经网络(FNN)的赤潮预警预测研究

为研究各种理化因子与赤潮藻类浓度间的非线性对应规律和有效预测赤潮藻类浓度,构建了基于BP 算法的一个四层模糊神经网络模型。将模糊神经网络(FNN)技术引入赤潮预测研究,并与普通 BP 网络、RBF 网络的结果作比较,结果表明,该模型能够较好地反演出各种理化因子与夜光藻密度的非线性对应变化规律,有更好的预测功能。     

辽宁省春玉米全育期的气候适宜度及年景评估

为评价气候变化对玉米生长的影响,以辽宁省为例选取1969—2018年18个气象站点的逐日实测气象数据,利用模糊数学法建立春玉米气候适宜度评估模型,以地理信息技术为依托,探究春玉米气候适宜度的时空特征,并在此基础上进行玉米气候年景的综合评估。结果表明：① 辽宁省春玉米全生育期内日照、温度、降水适宜度波动幅度较大;然而春玉米种植气候适宜度的空间差异性较弱。② 春玉米各生育期气候适宜度由高到低为：出苗期>开花期>成熟期>播种期。全生育期温度适宜度最高,日照适宜度次之,降水适宜度最低。③ 春玉米播种期、出苗期、开花期和成熟期的气候适宜度最高值分别出现在辽阳、葫芦岛、营口和铁岭。④ 春玉米气候年景准确率达64%,表明该评估方法可以较为准确地反映气候年景。近50 a辽宁省春玉米偏好的年景有4个年份（1971、1979、1993、1998年）,较差的有1969年（4.98%）及1973年（5.59%）。