Prediction of Seaward Slope Recession in Berm Breakwaters Using M5' Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim’s and Shekari’s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5'' model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5'' approach.     

三峡库区四方碑滑坡稳定性与变形趋势预测

三峡水库建成后,库水位周期性涨落和暴雨产生的渗流作用导致大量古滑坡的复活或新滑坡的发生。以库区近水平层状结构的四方碑滑坡为例,依据库水位实际调动,将水位从175 m至145 m不同降速与50年一遇暴雨进行工况组合,计算4种工况下滑坡的稳定性及破坏概率。然后采用Geo-studio软件的Sigma模块对滑坡进行变形模拟,运用R/S分析方法判断滑坡的变形持续性,并结合野外调查情况,综合评价分析四方碑滑坡的稳定性。结果表明:滑坡在各工况下整体均处于基本稳定状态,具有低危险性;变形模拟结果显示滑坡前缘位移最大,与野外调查情况一致;各监测点Hurst指数均介于0.5~1,表明时间序列具有正持续性,在研究的时间限度内滑坡的局部破坏增强,应在汛期加强对滑坡前缘的巡查和预警。     

基于梯度提升算法的岩性识别方法

传统的岩性识别方法如岩屑录井、钻井取心及测井资料解释等技术,对录井质量的依赖程度较高,识别精度与效率低,泛化能力差。随着计算机技术的迅速发展,将测井资料与计算机技术相结合开展岩性研究已成为岩性识别的有效手段。本文提出了一种基于梯度提升算法XGBoost和LightGBM的岩性识别方法。以苏里格气田苏东41-33区块下碳酸盐岩储层为例进行测试验证,采用该方法结合测井资料中的声波时差、自然伽马、光电吸收截面指数、密度、深侧向电阻率和补偿中子等6种参数进行岩性识别,并与KNN （K近邻分类器）、朴素贝叶斯和支持向量机等传统算法进行对比,结果表明,3种传统算法的岩性识别准确率分别为78.45%、74.43%和78.72%,基于梯度提升算法XGBoost和LightGBM的识别准确率分别达到了98.90%和98.72%,远高于传统算法。     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

气温变化对西峰黄土高原地温与梨树发育期的影响

用西北地区140站1961-2000年的气温资料和西峰黄土高原1971-2005年5,10,15,20cm地温和1984-2005年梨树发育期资料,分析了西北地区春季、夏季、秋季、冬季和年气温的变化事实,再用相关计算和典型年份对比,分析了地温的时间变化规律及其对梨树发育期的影响。结果表明,西北地区20世纪60年代冬季增温,其余降温,70年代均降温,80年代冬季增温,其余降温,90年代均增温,冬季的最明显。西峰10cm地温各季节呈持续升高的趋势,春季增温最明显为0.058℃/a,变幅也最大,冬季增温幅度次之为0.039℃/a,再是秋季为0.032℃/a,夏季增温幅度最小为0.029℃/a,上升趋势均通过0.05和0.01的信度检验。冬季、春季地温与梨树的各发育期均为负相关,即地温高,发育期早,地温低,发育期迟。冬季地温与梨树发育期相关最显著的是叶变始期和开花始期,相关系数为-0.41～-0.52,信度为0.05,春季地温与之相关最显著的是开花始期,相关系数为-0.68～-0.69,信度达0.001。春季地温对梨树发育期的影响具有明显的持续性和滞后性,冬季地温对梨树发育期影响有阶段性,春季地温对梨树发育期的影响比冬季的明显。     

青岛奥帆基地海域漂浮浒苔光合生理特点研究

研究了青岛奥帆基地海域漂浮浒苔Enteromorpha prolifera （Muell）J.Ag.色素组成及含量、光合放氧及呼吸耗氧速率、P／R（净光合放氧速率／净呼吸耗氧速率）、抗氰呼吸等各项生理指标。结果表明,该海域漂浮浒苔的各项生理指标都在正常范围内,且有抗氰呼吸的存在,生长状况良好。     

Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle

A randomized kinodynamic path planning algorithm based on the incremental sampling-based method is proposed here as the state-of-the-art in this field applicable in an autonomous underwater vehicle. Designing a feasible path for this vehicle from an initial position and velocity to a target position and velocity in three-dimensional spaces by considering the kinematic constraints such as obstacles avoidance and dynamic constraints such as hard bounds and non-holonomic characteristic of AUV are the main motivation of this research. For this purpose, a closed-loop rapidly-exploring random tree (CL-RRT) algorithm is presented. This CL-RRT consists of three tightly coupled components: a RRT algorithm, three fuzzy proportional-derivative controllers for heading and diving control and a six degree-of-freedom nonlinear AUV model. The branches of CL-RRT are expanded in the configuration space by considering the kinodynamic constraints of AUV. The feasibility of each branch and random offspring vertex in the CL-RRT is checked against the mentioned constraints of AUV. Next, if the planned branch is feasible by the AUV, then the control signals and related vertex are recorded through the path planner to design the final path. This proposed algorithm is implemented on a single board computer (SBC) through the xPC Target and then four test-cases are designed in 3D space. The results of the processor-in-the-loop tests are compared by the conventional RRT and indicate that the proposed CL-RRT not only in a rapid manner plans an initial path, but also the planned path is feasible by the AUV.     

锁块式水下采油树井口连接器性能分析

水下采油树井口连接器是连接采油树和井口的关键设备,对深海水下勘探开发及采油树的安装连接具有不可或缺的重要意义。以一种锁块式水下连接器为例,介绍其结构组成和工作原理,通过有限元建立连接器的三维模型,分析连接器在下放安装工况、连接锁紧工况、正常生产工况和钻完井工况下的连接性能,得出井口与采油树本体接触面作用力、VX钢圈接触应力和锁块受力情况随外部载荷的相互关系。结果表明,VX钢圈在整个过程中都能够保持良好密封,井口与采油树本体接触面未发生分离,弯矩对连接器连接性能影响很大,在额定弯矩作用时连接器部分锁块已发生弹塑性失效。     

结合MST划分和RHMRF-FCM算法的高分辨率遥感图像分割

针对基于像素的HMRF-FCM算法抗噪性差以及对地物复杂边界分割精度低的问题,提出一种结合形状信息的静态MST区域划分和RHMRF-FCM算法的高分辨率遥感图像分割方法。该方法定义一种静态MST同质区域划分准则,借助MST能较好表达边界和形状信息、能较好抑制几何噪声的特点,解决地物复杂边界的表达和降低分割结果中几何噪声问题。首先,利用MST静态划分将图像域划分成若干个均质区域,假设每个均质区域内光谱测度服从独立同一的多元高斯分布。然后,在此基础上构建了区域隐马尔可夫随机场模型,以及建立基于信息熵和KL信息正则化项的模糊聚类目标函数。最后,采用偏微分方法对分割模型参数进行求解,从而得到全局最优分割结果。为验证本文方法,对WorldView-3高分遥感图像进行分割试验。定性、定量分析了尺度参数、光谱相似性参数和区域紧致度参数对最优分割结果的影响,并对比分析本文算法和eCognition软件中的多分辨率分割算法、分水岭算法。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.