Prediction of longitudinal dispersion coefficient using multivariate adaptive regression splines

In this paper, multivariate adaptive regression splines (MARS) was developed as a novel soft-computing technique for predicting longitudinal dispersion coefficient (D_L) in rivers. As mentioned in the literature, experimental dataset related to D_L was collected and used for preparing MARS model. Results of MARS model were compared with multi-layer neural network model and empirical formulas. To define the most effective parameters on D_L, the Gamma test was used. Performance of MARS model was assessed by calculation of standard error indices. Error indices showed that MARS model has suitable performance and is more accurate compared to multi-layer neural network model and empirical formulas. Results of the Gamma test and MARS model showed that flow depth (H) and ratio of the mean velocity to shear velocity (u/u^?) were the most effective parameters on the D_L.     

Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms

A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R ² and RMSE.     

梯形断面明渠中纵向离散系数研究

基于最大信息熵原理,提出了一种确定梯形断面纵向流速分布的方法,研究了梯形断面明渠中流动横向不均匀和垂向不均匀对纵向离散的影响,建立了一个针对梯形断面明渠流动的纵向离散系数计算公式。公式将纵向离散系数与反映断面流速分布不均匀和壁面影响的参数建立联系,机理更加清楚,预测结果与其他学者相应的实验结果吻合良好,该方法理论推导过程严密,不依赖于特定的试验结果或实际测量资料,为梯形断面明渠的污染物混合输移过程中参数确定提供了有效的方法和途径。     

基于贝叶斯网络的地震液化概率预测分析

基于解释结构模型和因果图法,选取12个具有代表性的定性和定量因素,在大量数据不完备的情况下提出了建立贝叶斯网络液化模型的方法。以2011年日本东北地区太平洋近海地震液化不完备数据为例,采用总体精度、ROC曲线下面积、准确率、召回率和F1值5项指标对模型进行综合评估,并与径向基神经网络模型进行对比。结果表明：贝叶斯网络液化模型的回判和预测效果都优于径向基神经网络模型,且对于数据缺失的样本的预测效果也较理想。此外,该模型对于不同土质的液化评估均有较好的适用性。分类不均衡和抽样偏差会对模型的学习和预测效果产生很大影响,建议应同时采用上述5项评估指标进行综合评估模型的优劣。     

Predicting surf zone injuries along the Delaware coast using a Bayesian network

Natural Hazards - More than 2000 surf zone injury (SZI) events, including 196 spinal injuries and 6 fatalities, were recorded at the five most populated beaches along the 25 miles of...     

顺直河流横向紊动扩散系数

利用垂直紊动扩散系数及水利几何形态关系,借助于抛物线型断面形态方程,提出了顺直河道中局部水深沿横断面的分布,在此基础上确定了横向紊动扩散系数的断面分布及其平均值表达式,阐明了无量纲横向紊流扩散系数间的关系,计算的断面平均横向紊动扩散系数与138组试验资料吻合良好。比较结果表明,建立的顺直河道横向紊流扩散系数计算公式能给出与实测值最接近的预测值。与现有的其它横向紊流扩散系数计算公式相比,其公式在理论上更加合理,机理上更加清楚,并且具有最小的预测误差。     

Prediction of pollutant dispersion in a street canyon by CFD

Urban pollution dispersion has been studied extensively with the rapid increase of traffic. The Computational Fluid Dynamics （CFD） approach is used increasingly to simulate the pollutant dispersion in order to replace the costly experiment. As a benchmark experiment, the results of pollutant dispersion in a street canyon are often used to validate numerical models. The following problems are usually neglected： （1） How to distinguish the open country street canyon （shortened as OCSC） and urban roughness street canyon （shortened as URSC） and to determine computation field; （2） How to give the value of turbulent kinetic energy, k, and dissipation rate, e, at inlet boundary. The aim of the research is to solve the above problems. A two-dimensional numerical model based on the Reynolds-averaged Navier-Stokes equations coupled with RNG k-e turbulence model is adopted in this study. The main conclusions are： （1） For OCSC, two side buildings should be inside the computation domain. The inlet and outlet boundary should be set up at some distance from the fight and left buildings, respectively. The distance between the inlet boundary and the fight building is at least three times the height of the fight building because the incoming velocity, k and e, need some distance to be fully developed. （2） For the simulation of pollutant dispersion in URSC, it can be simplified the air flowing over upwind canyons as it flows on very rough ground. Then, the inlet and outlet boundary can be set up at the roof of fight and left buildings. The results of pollutant dispersion in the 10th street canyon out of 16 street canyons show the correctness of above simplification. （3） The determination of k and e value at the inlet boundary is sometimes difficult. Our results show the values of k and e are very important to get good results.     

纵向弥散作用与渗透介质非均质性定量关系的模拟研究

在地下水污染模拟预报中,弥散参数是很难确定的一个模型参数。因实验室小尺度弥散规律一般不能用于大尺度弥散过程,而野外示踪试验却耗资大、周期长,限制了其实用性。文中利用随机数值模拟手段、基于随机理论的蒙特卡罗方法及序贯高斯模拟技术来生成渗透系数随机场,并研究渗透系数对数场的方差、相关长度以及变异函数类型在不同尺度上对纵向弥散度的影响,进而建立纵向弥散度与随机分布渗透系数场的方差和相关长度的统计定量关系,并与Gelhar理论计算结果进行比较。数值模拟结果表明,经过一定迁移距离后纵向弥散度与随机分布渗透系数对数场的方差和相关长度具有良好的线性统计关系,与Gelhar理论公式表达的关系类型类似。但对于较大的方差,纵向弥散度模拟结果明显大于Gelhar理论计算值,而对于较大相关长度在迁移距离不很大时,纵向弥散度模拟结果明显小于Gelhar理论计算值。本研究可为野外大尺度地下水污染预报模型中水动力弥散参数的确定提供方法借鉴。     

Correction to: Predicting surf zone injuries along the Delaware coast using a Bayesian network

Natural Hazards - The article was published with errors.     

Prediction and controlling of flyrock in blasting operation using artificial neural network

Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.     

Prediction method of sand-movement direction in rivers by geochemical elements

 The analysis of sand samples by X-ray fluorescent spectroscopy (XRF) gives the ratio of the geochemical elements to construct the sand samples. The application of the ratio matching to sand samples represents the correlation number between two sand samples with respect to geochemical elements. When the correlation number between two sand samples is low, the two samples are not geochemically similar each other. This denotes that the exchange of sand between two sampling points is scarce or the two samples are independent. When the correlation number between two sand samples is high, the two samples are geochemically similar, signifying that the exchange of sand between two sampling points is frequent or there is sand movement between two sampling points. If there exists prominent sand movement in the study area, the correlation number is almost 1 and kilo count number per second of each geochemical element per weight decreases along the flow direction. The decrease is caused by the reduction of sizes and the adherence of dirt on the surface of sand particles. Since the flow direction in rivers is usually the same as the direction of sand movement, it can be verified. This study obtains satisfactory results applying the method of prediction in sand movement to sediments in rivers. Received: 17 May 1995 · Accepted: 14 August 1995     

Prediction of tropical cyclone frequency with a wavelet neural network model incorporating natural orthogonal expansion and combined weights

A nonlinear wavelet neural network (WNN) model with natural orthogonal expansion (NOE) and combined weights is constructed to predict the annual frequency of tropical cyclones (TCF) occurring over the coastal regions of Southern China. Combined weights are obtained by calculating categorical weights, based on the particle swarm projection pursuit, and ranking weights, based on fuzzy mathematics, followed by optimization. The global monthly mean heights at 500?hPa and sea-surface temperature fields are used as two predictors. The linear and nonlinear information of the predictors with reduced dimensions is gathered through the NOE and combined weights, respectively, and treated as the input into the WNN model. This model is first trained with the 55-year (i.e., 1950?C2004) TCF data and then used to predict annual TCFs for the subsequent 5?years (i.e., 2005?C2009). Results show that the mean absolute and relative errors are 0.6175 and 9.34?%, respectively. The impacts of the combined weights, NOE and WNN as well as the traditional multi-regression approach on the TCF prediction are examined. Results show superior performance of the WNN-based model in the annual TCF prediction.     

Prediction of residual friction angle of clays using artificial neural network

The residual strength of clay is very important to evaluate long term stability of proposed and existing slopes and for remedial measure for failure slopes. Various attempts have been made to correlate the residual friction angle (_r) with index properties of soil. This paper presents a neural network model to predict the residual friction angle based on clay fraction and Atterberg's limits. Different sensitivity analysis was made to find out the important parameters affecting the residual friction angle. Emphasis is placed on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of soil properties on the residual shear angle. A prediction model equation is established with the weights of the neural network as the model parameters.     

Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

Rock physical parameters such as porosity and water saturation play an important role in the mechanical behavior of hydrocarbon reservoir rocks. A valid and reliable prediction of these parameters from seismic data is essential for reservoir characterization, management, and also geomechanical modeling. In this paper, the application of conventional methods such as Bayesian inversion and computational intelligence methods, namely support vector regression (SVR) optimized by particle swarm optimization (PSO) and adaptive network-based fuzzy inference system-subtractive clustering method (ANFIS-SCM), is demonstrated to predict porosity and water saturation. The prediction abilities offered by Bayesian inversion, SVR-PSO, and ANFIS-SCM were presented using a synthetic dataset and field data available from a gas carbonate reservoir in Iran. In these models, seismic pre-stack data and attributes were utilized as the input parameters, while the porosity and water saturation were the output parameters. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential for indirect estimation of porosity and water saturation with high degree of accuracy and robustness from seismic data and attributes in both synthetic and real cases of this study.     

天然河湾几何形态统计分析

天然河湾是弯曲河流重要的地貌组成单元,研究河湾的几何形态和演变规律具有较高的理论价值。运用Google Earth卫星图像和AutoCAD软件相结合的方法,选取不同区域的8条河流的136个山区河湾和325个冲积河湾作为统计样本,定义和测量河湾几何形态参数。统计分析表明,山区河湾弯曲度变化区间为[1.7,14.8],冲积河湾弯曲度变化区间为[1.6,38.5]。冲积河湾的弯曲度均值和标准方差都大于山区河湾。山区河湾的弯顶偏向角无明显的趋向性,弯顶向上游发育和向下游发育的机率基本是相同的。冲积河湾的弯顶偏向角大于85°的河湾占65.5%,表明大多数冲积河湾的弯顶是偏向上游发育的。冲积河湾平均河宽与弯顶河宽成较好的线性关系,平均河宽与颈口宽度具有一定程度的正相关关系。天然河湾的横向相对偏移度与弯曲度成较好的线性关系,两者表征河湾平面弯曲形态具有一致性。     

瞬变电磁测深的微分电导成像

通过对等效导电平面方法原理的阐述,指出了利用视纵向电导参数进行瞬变电磁测深资料解释的优越性。同时,为了提高对电性层位的分辨能力,在明确电导及其导数曲线物理含义的基础上,采用B样条函数对实测数据进行处理,实现了电导参数的微分成像。实践证明,采用该方法的成像结果与其他反演结果吻合较好,其“电性同相轴”以直观、形象的面貌清晰地展示了电性界面的分布形态。     

A new numerical method of considering local longitudinal dispersion in single fractures

The solutions of advection–dispersion equation in single fractures were carefully reviewed, and their relationships were addressed. The classic solution, which represents the resident or flux concentration within the semi‐infinite fractures under constant concentration or flux boundary conditions, respectively, describes the effluent concentration for a finite fracture. In addition, it also predicts the cumulative distribution of solute particle residence time passing through a single fracture under pulse injection condition, based on which a particle tracking approach was developed to simulate the local advection–dispersion in single fractures. We applied the proposed method to investigate the influence of local dispersion in single fractures on the macrodispersion in different fracture systems with relatively high fracture density. The results show that the effects of local dispersion on macrodispersion are dependent on the heterogeneity of fracture system, but generally the local dispersion plays limited roles on marodispersion at least in dense fracture network. This trend was in agreement with the macrodispersion in heterogeneous porous media. Copyright © 2013 John Wiley & Sons, Ltd.     

岩体裂隙网络的不均匀系数

本文应用多标度分形理论,提出了描述岩体裂隙网络中裂隙分布不均匀性的新指标--岩体裂隙网络的不均匀系数。研究表明,该指标可以充分反映岩体裂隙网络的不均匀程度     

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.     

用BP网络求解土体的导热系数

以人工神经网络为基本工具,利用其强大的非线性映射能力,综合考虑土体的物理性质指标对其导热性能的影响,为求解土体的工程参数提供了一条新的途径。结果表明,ＢＰ网络能够充分体现土体物理指标之间的非线性关系和隐含关系,具有较高的求解能力,所求出的土体的导热系数与实测值接近,其精度远高于线性回归,且简单实用。