M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R2 = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.

基于改进M5'-主成分模型树的高心墙堆石坝沉降变形预测

针对高心墙堆石坝沉降变形过程动态非线性特点,建立基于改进M5'-主成分模型树的高心墙堆石坝沉降变形分析模型,在采用相关性分析甄选沉降变形影响因素和采用主成分分析将高维影响因素空间进行降维的基础上,利用该全局分段非线性模型对高心墙堆石坝沉降过程进行分析。通过与沉降量实测值对比,验证了改进M5'-主成分模型树的有效性。通过绝对差值和均方根误差2个指标对比分析改进M5'-主成分模型树与M5'模型树、多元线性回归模型、主成分回归分析模型的预测结果,表明改进M5'-主成分模型树预测沉降量具有更高的精度。

采用M5'模型树和测量数据识别抽汽式机组汽耗量特性

汽轮机组特性随着机组老化而变化,传统上采用定期现场实测,需停机和采用专门的设备与系统,费用很高,因此基于现有自动化系统历史测量数据的特性曲线识别方法十分必要。一般汽轮机组汽耗量特性具有非凸和非连续等特点,常规的多元线性回归拟合不能适应。M5’模型树算法是一种多输入单输出系统的分段线性化的数据挖掘算法。提出采用M5’模型树的抽汽式机组汽耗量特性模型和其模型结构及参数识别算法,用于滚动利用最新的电厂测量历史数据获取最新的汽耗量特性。该方法简单、有效,逼近能力强,自动化程度高,在处理非凸形和非连续性的特性方程具有优势。通过多个热电厂的实时数据进行验证,具有很高的预测精度,效果优于多元线性回归拟合方程。

M5’模型树在热电厂负荷优化中的应用

机组汽耗特性是热电厂负荷优化分配的基础。为了改进传统单一多元线性回归模型无法适应非凸、非连续的汽耗特性,本文基于M5’模型树算法,滚动利用机组自动化系统的最新历史数据获取最新汽耗特性,并在此基础上建立实时厂级负荷优化模型,给出了应用差分算法求解全局最优解的方法。实例表明:所建立的汽耗特性模型的预测能力强且建模过程可控,方便应用于热电厂的负荷优化,具有实用价值。

M5'模型树算法和无人机航拍影像在震后崩塌滑坡分析中的应用——以鲁甸M_S6.5地震为例

利用高分辨率无人机航拍影像,结合基本地质资料,分析了影响2014年8月3日鲁甸M_S6.5地震震后崩塌滑坡分布的主要因素,使用M5'模型树算法建立了崩塌滑坡密度与其影响因子间的分段线性模型,并检验了该模型的预测性能。结果表明,地震诱发的崩塌滑坡分布受断层距、岩土体结构强度、坡度、植被条件等的影响,其中,断层距、岩土体结构强度及坡度等为主要影响因素;崩塌滑坡易发生在结构破裂区及坡度为38°~50°的区域,其分布密度随断层距的增加而减小;利用M5'模型树算法建立的模型体现出崩塌滑坡分布与其影响因子间复杂的非线性关系,模型检验结果显示,理论模型与实际关联函数间的相关系数达到0.88,因此,可利用该模型预测地震诱发的崩塌滑坡的分布。

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.

A Simple Predictive Performance Model of Solar Cell under Very Hot and Humide Climate

In the scope of solar energy-based electrical needs in rural tropical regions, the present article develops and confronts experimental power models from the using of manufacturer data and a new model made with the meteorological and electrical data acquired. These data are registered through an acquisition station around a monocrystalline photovoltaic panel, designed and realized in the scope of this work. After the acquisition of meteorological data, a choice of the most relevant meteorological variable as input vectors to express the output powers obtained was carried out. Around the Single-Diode model, seven models are performed with analytics equations, iterative methods and an optimization method with a multi-objective function to get internal parameters. The proposed experimental model is made by a combination of the solution got at STC of an iterative method, with the value of nameplate and the use of an open circuit voltage equation with experimental coefficient to predict power output in operating conditions, and it’s demonstrated more efficient. The optimization of a multi-objective function using Nonlinear Squares (NLS) through the Leveng-Marqued method to solve the parameter estimation of a PV panel has been well done and the results are useful, like classic iterative method and less time-consuming.

GEM模型、RF模型和M5T模型的无人机DEM测点插值算法比较

无人机技术作为现如今测量的先进技术,已被逐渐广泛应用,由于DEM数据在实测过程中只能形成点数据,因此对点数据的插值计算一直是测绘专业研究的热点。为找出适用于无人机DEM测点插值计算的最优模型,本文基于GEM模型、RF模型和M5T模型,以降雨、经纬度和海拔为输入参数,对不同模型下的DEM进行了插值计算,结果表明:GEM模型表现的精度最高,该模型RMSE仅为11.72 m,RRMSE为12.7%,R2为0.964,Ens为0.946,MAE为11.28 m,该模型可作为无人机DEM测点插值计算的标准模型使用。

Comparative Study of the Performance of M5-Rules Algorithm with Different Algorithms

The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The determination of the value of this variable when initiating software projects allows us to plan adequately any forthcoming activities. As far as estimation and prediction is concerned there is still a number of unsolved problems and errors. To obtain good results it is essential to take into consideration any previous projects. Estimating the effort with a high grade of reliability is a problem which has not yet been solved and even the project manager has to deal with it since the beginning. In this study, performance of M5-Rules Algorithm, single conjunctive rule learner and decision table majority classifier are experimented for modeling of Effort Estimation of Software Projects and performance of developed models is compared with the existing algorithms namely Halstead, Walston-Felix, Bailey-Basili, Doty in terms of MAE and RMSE. The proposed techniques are run in the WEKA environment for building the model structure for software effort and the formulae of existing models are calculated in the MATLAB environment. The performance evaluation criteria are based on MAE and RMSE. The result shows that the M5-Rules have the best performance and can be used for the effort estimation of all types of software projects.

Modeling oblique load carrying capacity of batter pile groups using neural network,random forest regression and M5 model tree

M5 model tree,random forest regression(RF)and neural network(NN)based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups.Pile length(L),angle of oblique load(a),sand density(ρ),number of batter piles(B),and number of vertical piles(V)as input and oblique load(Q)as output was used.Results suggest improved performance by RF regression for both pile groups.M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also.Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data.NN based approach was found performing equally well with both smooth and rough piles.Sensitivity analysis using all three modelling approaches suggest angle of oblique load(a)and number of batter pile(B)affect the oblique load capacity for both smooth and rough pile groups.

Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET_(0) using machine learning models have not been reported yet.Besides,machine learning models such as the XGBoost model has multiple parameters that need to be tuned,and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution.This study investigated the performance of the hybrid extreme gradient boosting(XGBoost)model coupled with the Grey Wolf Optimizer(GWO)algorithm for forecasting multi-step ahead ET_(0)(1-3 months ahead),compared with three conventional machine learning models,i.e.,standalone XGBoost,multi-layer perceptron(MLP)and M5 model tree(M5)models in the subtropical zone of China.The results showed that theGWO-XGB model generally performed better than the other three machine learning models in forecasting 1-3 months ahead ET_(0),followed by the XGB,M5 and MLP models with very small differences among the three models.The GWO-XGB model performed best in autumn,while the MLP model performed slightly better than the other three models in summer.It is thus suggested to apply the MLP model for ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.

防淤积型透空式防波堤波浪爬高试验及预测研究

波浪爬高是防波堤堤顶高程设计和预估工程造价的重要影响因素.以防淤积型透空式防波堤为研究对象,采用1∶10的模型比尺,在64组不同工况下开展防波堤的物理模型爬高试验,并记录了不同工况下防波堤迎浪侧的最大波浪爬升高度,探究了防波堤的波浪爬升特征.分别运用多项式回归方法、速度水头方法与M5'模型树算法并结合物理模型试验波浪爬高结果,发展透空式防波堤波浪爬高的预测公式.利用统计参数对3种方法发展的预测公式进行对比分析.结果表明:透空式防波堤背浪侧的波面变化滞后于迎浪侧,其最大波浪爬高小于迎浪侧,防波堤具有良好的消波效果;采用多项式回归方法与速度水头方法发展的预测公式能够在一定程度上实现对防波堤波浪爬高的预测,前者仅考虑了波高而忽略了其他波浪条件及防波堤结构参数对波浪爬升高度的影响,后者对波浪爬高的预测结果与试验值偏差较大,二者在实际应用过程中都存在一定的局限性;M5'模型树算法发展的波浪爬高预测公式利用无量纲参数综合了各项波浪要素以及防波堤的结构参数等,拥有良好的统计指数,能够更好地预测透空式防波堤的波浪爬高,具有较高的实际应用价值.

B-S-M期权定价模型和二叉树模型运用对比分析

B-S-M期权定价模型和二叉树模型作为两大期权定价方法,在理论和实际应用上有所差异,而且对认购虚值期权和认沽虚值期权,运用两种期权定价模型的效果也有所差异。本文以阿里巴巴(BABA)在纽交所上市的股票期权为例,分别利用B-S-M期权定价模型和二叉树模型为阿里巴巴股票2021年12月17日即将到期的虚值认购期权和虚值认沽期权进行期权定价。研究结果表明:两种期权定价模型对看跌虚值期权的定价效率更高,而两种期权定价模型中B-S-M的定价效率更高。

综合方法对烤烟化学成分和烟气组分的相关分析

针对烟草化学成分与烟气组分间存在较复杂的非线性关系,采用主成分分析法进行相关性分析。以5个产区烤烟为样本,利用投影、主成分提取来分析影响烟气的主要化学成分。采用M5'模型树法建立烟气成分的分段线性模型。根据2种方法的结论,更有效判断烤烟化学成分对烟气成分的影响,提高对烤烟烟气成分的预测精度。

多光谱影像的陕西大西沟矿区土壤重金属含量反演

传统的以“点采样+实验室分析”为主的土壤重金属含量分析技术成本高、效率低下,而基于多光谱遥感的土壤重金属高精度定量反演中存在重金属含量影响因子的优化这一难题,以陕西大西沟矿区这类山区地形条件下的金属矿区为例,利用Landsat8/OLI多光谱卫星影像、DEM数据以及外业土壤采样分析数据,开展了矿区土壤重金属含量指示因子分析及定量反演研究。首先,考虑研究区地形地貌特点,设计了沿研究区地形特征线及其两侧坡面均匀分布的样点分布方案,采集了45个样本。并对45个样本的混合样中的8种重金属含量进行了兴趣度分析,根据含量超标程度及矿的类型选取了铜、铅、砷3种元素作为分析对象。其次,根据研究区土地利用现状及地形特点,提出了以Landsat8/OLI影像B2至B7波段光谱反射率、粘土矿物比(CMR)、改进归一化水体指数(MNDWI)、差异植被指数(DVI)等八种光谱指数、以及反映研究区地形坡度和坡向三类因子作为反映土壤重金属含量空间分布特征的侯选因子。进而,对上述三类侯选因子与样本中3种金属含量进行了最小二乘相关性分析。根据分析结果,引入了基于估算误差最小准则的金属含量估算模型——基于规则的M5模型树的分段线性估算模型。以上述三大类共17个指示因子作为模型的输入,利用80%的土壤样本分析数据作为模型的训练数据,经过M5模型树的构建、平滑和树枝修剪过程,建立了3种金属的反演模型实现了研究区中土壤中3种金属含量的估算。同时,基于均方根误差(RMSE)最小准则确定了以光谱因子为主的最利于反演的最佳指示因子集。最后,用随机选取的20%的检验样本对模型进行了反演精度分析,验证了该模型对铜、铅、砷3种金属含量的反演精度比普通的线性模型分别提高了27.3%,24.6%,20.9%,同时,铜、铅元素的可信度也有所提高。利用上述模型的反演结果实现了3种金属含量的空间分布制图,并将反演结果与1990年公布的国家土壤元素背景值进行了对比。此外,分析了研究区铜、铅、砷3种金属的空间分布规律,并利用野外调查结果进行了验证。

多进制小波和二叉树实现大规模地形的实时漫游

针对大规模地形的实时绘制是三维可视化领域中研究的热点和难点 ,提出一种视相关的、基于M(M≥ 2 )进制小波压缩和二叉树分块的优化算法 ,来构建地形多分辨率模型 该算法给出了一种依赖于地形的起伏状况、视点位置和视距的数据压缩和网格剖分的方法 ,并且解决了不同尺度结点间的“裂缝”问题 实验表明 ,该算法具有消耗内存少、建模速度快、保真性好等优点 。

用两台装置找出两个坏硬币的最优化方法

本文探讨从一堆硬币中以最少次数用规定的方法挑选出其中坏硬币的问题。由于给定的模型不同,这一问题有多种不同的形式。其中,用一台装置挑选出至多两个坏硬币的问题已得到广泛研究,且已有了一些好结果。本文考虑的是用两台平行装置挑选两个坏硬币的情形。我们特别考虑了装置只能区分是否在某装置中同时放入了两个坏硬币这一模型M。以t_k表示关于模型M的一个过程t在k次测试中所能鉴别的最大硬币数目,以n_k表示关于模型M在k次测试中所能鉴别的最大硬币数目。我们找到了关于模型M的一个过程t,使得t_k/n_k>0.994(k≥20)。

基于电厂能效监测平台的机组特性在线识别与可视化

基于省级热电厂能效监测平台的运行数据,采用M5′模型树方法在线识别和绘制了较为精确、详细的单抽和双抽机组工况图。该方法采用分段式线性拟合,自动优选分段数并逼近可能非凸或非连续的非线性耗量曲线。利用该方法进行机组定期滚动识别对比,可以更好地察觉机组特性的变化,为机组能效监视提供可视化感知工具,为测量数据提供校验依据,并为电厂热电负荷优化分配和电网节能调度提供精确的分段线性化机组特性函数。

华北地区强震重复性模式的讨论

根据位错量与地震应变能的关系及岩石不均匀脆性材料在受力后小破裂增加的特点,本文对华北这一相对完整块体的强震、强震丛、中强地震的活动参数与发震时间间隔进行了研究,初步建立了一种强震重复性的非线性简化模式,对强震丛之前中强地震的时间分布及物理力学过程有了一个大概的认识。在此基础上,讨论了中长期地震预测中的两个具体问题:①如何估计地震活动高潮时段开始时间;②高潮时段强震规模与结构的估计。