基于优化的BP神经网络地层可钻性预测模型

提出了一种粒子群算法（PSO）优化的BP网络模型预测地层可钻性的新方法。利用粒子群算法优化BP网络模型的参数,避免了BP网络陷入局部极小值的缺点,提高了模型的预测速度和精度。结合钻探实例,利用测井资料和地层可钻性级别的关系建立了可钻性级别实时预测模型,并将该模型与传统的BP网络进行对比,结果表明,该模型优于BP网络,具有较高的精度和较快的收敛速度,有一定的适用性。     

基于差异进化算法的前馈神经网络在大坝变形监测中的应用

针对当前大坝安全监测中广泛采用的回归模型欠拟合的不足,提出了基于差异进化算法的前馈神经网络模型。差异进化算法是基于种群策略的全局优化搜索算法,具有应用简单、收敛快的优点。采用该法训练的神经网络可以有效避免常规BP(back propagation)神经网络收敛于局部极小点的缺陷。将提出的方法应用于某拱坝的变形监测,通过计算表明,应用DE(differential evotntion)神经网络模型预报大坝变形的精度比常规回归模型和BP神经网络模型均有所提高。     

基于进化神经网络混凝土大坝变形预测

根据丰满大坝多年变形观测数据,建立了基于进化神经网络混凝土大坝变形预测方法。经典的BP神经网络的缺陷在于收敛速度慢和泛化能力弱等特性。与普通的多元回归方法和传统的BP神经网络相比,采用遗传算法训练的人工神经网络预测模型预报大坝的变形具有精度高和全局收敛的特点。在丰满大坝工程实际应用表明,所建立的基于进化神经网络混凝土大坝变形预报方法与广泛采用的统计方法相比,可以显著提高大坝变形预报精度。     

基于遗传神经网络的渗流实时预报方法研究

考虑渗流的主要影响因子,运用遗传神经网络建立了大坝渗流实时预报模型。该模型具有再学习能力,在应用过程中,可以用新的观测资料对模型不断地进行学习训练,且随着样本的积累,模型预报精度不断提高,预报速度很快,因而完全满足实时预报的需要。利用该模型对丰满大坝横向扬压力进行了建模预报,预报值与实测值基本吻合,预测精度高,证明该方法用于大坝渗流实时预报的有效性和实用性。     

基于粒子群优化的BP神经网络模型参考自适应控制系统

将粒子群优化的BP神经网络作为模型，参考自适应控制系统的控制器，把参考模型输出与系统实际输出的均方误差作为PSO-BP神经网络的适应函数，通过PSO算法强大的搜索性能使自适应控制系统的均方误差最小化。仿真实例结果表明，基于粒子群优化算法的BP神经网络自适应控制系统收敛快、精度高，有较好的网络的泛化和适应能力，能够很好地控制系统的输出跟随参考模型的输出。     

基于粒子群-变分模态分解、非线性自回归神经网络与门控循环单元的滑坡位移动态预测模型研究

以三峡库区八字门阶跃型滑坡为例,针对静态机器学习模型在周期项位移预测中的不足以及高频随机项位移预测困难等问题,提出了一种新的滑坡位移预测方法。基于时间序列分解思想,采用粒子群算法（PSO）对变分模态分解（VMD）进行参数寻优,并将位移时间序列分解为趋势项、周期项和随机项。趋势项主要受滑坡内部因素影响,采用傅里叶曲线进行拟合预测;周期项由外部因素导致,基于格兰杰因果检验进行成因分析,并引入一种对时间序列历史状态具有较高敏感性的非线性自回归神经网络（NARX）进行预测;随机项频率较高且影响因素无法判定,采用一维门控循环单元（GRU）进行预测。最后将各分量预测位移进行叠加重构,实现滑坡累计位移的预测。结果表明,提出的（PSO-VMD）-NARX-GRU滑坡位移动态预测模型精度较高,且各位移分量预测精度明显高于静态模型中BP神经网络、支持向量机（SVM）和传统自回归模型ARIMA,可为阶跃型滑坡位移预测提供参考。     

不同方法在感潮河段ADCP在线测流系统中应用的比较分析

在水力因素多变的长江下游感潮河段建立ADCP在线测流系统实时采集指标流速,可选用合适的方法推求断面平均流速,从而实现流量的实时报汛和整编。多元线性回归分析和BP神经网络具有原理明晰、实现便捷等特点,为比较以上两种方法在断面平均流速计算中的优劣,以南京水文实验站2014年以来实测数据为例,分析不同情况下两种模型的拟合精度和预测精度。结果表明,两种模型均具有较好的有效性、精确性和稳定性,且拟合精度与模型选用的监测指标有关;对于只采用单一指标流速而言,BP神经网络模型的结果明显优于多元线性回归模型。同时,两种模型都能较好的预测断面平均流速,其中BP神经网络适应更好。     

非线性回归分析的PSO小波网络方法及应用

文章介绍了粒子群算法（PSO）和小波神经网络的基本原理，把基于粒子群小波网络的混合算法应用到非线性回归问题中，并对算法解决非线性回归问题进行了实践分析，最后建立了测井响应值和物性参数孔隙度之间的回归模型。从仿真结果可以看出，本方法的回归值和岩心分析值符合较好，表明粒子群小波网络进行非线性回归分析是一种有效的数据回归方法。     

基于人工神经网络的大坝变形分析与预报——以西津大坝27~#点的变形监测为例

以MATLAB语言为基础,应用BP神经网络、逐步回归分析进行西津大坝27#点的变形分析与预报研究.在此基础上,进一步提出了逐步回归BP神经网络组合的预报方法,并探讨了3种方法的预报结果.研究表明,BP神经网络用于大坝变形分析与预报是可行的,所提出的逐步回归BP神经网络组合法提高了变形影响因子选择的科学性,在预报效果上,优于前两种方法.     

三种基于神经网络的洪水实时预报方案的比较研究

在总结神经网络应用的基础上,归纳了3种基于神经网络的洪水实时预报方案。第一种是神经网络水文模型的模拟模式加模拟误差的自回归校正模型,第二种是权重系数固定的神经网络实时预报方案,第三种是权重系数自动更新的神经网络实时预报方案。采用10个不同流域的日流量资料对这3种方案进行率定和校核。比较这3种方案的实时预报精度。结果发现,第三种方案不仅预报精度要高于其他两种方案,而且比第一种方案少了一个自回归校正模型,结构简洁。本文建议采用第三种洪水实时预报方案。     

基于PSO-DSRVM的边坡变形预测

为了建立高精度的边坡位移预测模型,文章采用基于粒子群优化(PSO)的双稀疏相关向量机(DSRVM)建立边坡稳定性和影响因素之间的非线性关系。双稀疏相关向量机是在变分和相关向量机(RVM)框架下提出的一种多核组合优化的方法,相比于RVM和其他多核学习方法,DSRVM不仅有更少的训练时间,并且能够得到更高的预测精度。由于DSRVM的核参数对预测效果的影响较大,文章采用粒子群算法实现多个核参数的优化选取并应用于边坡位移预测。最后将本文提出的基于粒子群优化的双稀疏相关向量机(PSO-DSRVM)预测结果与极限学习机(ELM)和小波神经网络(WNN)预测结果进行对比,通过均方根误差(RMSE)、复相关系数(R²)和平均相对预测误差(ARPE)进行评价,验证了PSO-DSRVM模型在边坡变形预测上的可行性。     

大坝安全诊断的混沌优化神经网络模型

为了提高大坝变形的预测精度,采用小波变换和分形理论对大坝位移观测数据的非线性动力学特性进行了分析,揭示了其具有低维混沌动力特性,这为大坝变形预测模型的建立提供了理论依据和先验知识。基于低维混沌动力特性,设计了能捕获大坝位移观测数据全局动力特性,兼具神经网络模型结构优化和动力机制时新的混沌优化神经网络大坝变形预测模型。在工程实例中,由多个度量指标组成量化评价体系,对模型预测性能进行综合评价,结果表明,所建模型比传统BP神经网络和ARMA模型具有更高的预测精度。     

GM（1，1）动态模型在吴江市地下水水位预测中的应用

以吴江市地下水水位预测为例,详细阐述了地下水水位时间序列的GM（1,1）动态模型的原理和建立过程,并根据模型的预测值和实测值,对模型的精度进行了检验,结果表明,模型的预测精度达到了99．27％,等级属于Ⅰ级,具有实际的应用价值,为地下水资源的科学管理提供了依据。     

基于长自回归残差法的时序分析在大坝变形监测与预报中的应用

本文主要研究了时间序列分析在大坝变形监测与预报中的应用。突出分析了ARMA模型的优点。分别利用最小二乘估计法和长自回归模型残差法进行坝体形变的分析和预报;通过对不同阶数ARMA模型在实际应用中的比较分析,确定出一种精度相对高、原理简单的方法进行建模,从而进行形变的分析和预报。     

GM—BP串联组合预测模型在滑坡变形监测中的应用

为了提高滑坡的预测精度,通过对灰色GM（1,1）模型与BP神经网络模型各自优缺点及互补性的分析,建立了GM—BP串联组合预测模型。模型首先采用等维动态GM（1,1）模型进行初步预测,然后利用BP神经网络对初步预测的结果进行训练及仿真,通过数据的归一化处理,参数的判定选取,获得组合模型预测值。以茅坪滑坡为例,对位移进行了预测。通过数据的对比分析,发现GM—BP串联组合预测模型在短期预测精度上高于单一模型。     

Use of neural networks for the prediction of frictional drag and transmission of axial load in horizontal wellbores

The use of mud motors and other tools to accomplish forward motion of the bit in extended reach and horizontal wells allows avoiding large amounts of torque caused by rotation of the whole drill string. The forward motion of the drill string, however, is resisted by excessive amount of friction. In the presence of large compressive axial loads, the drill pipe or coiled tubing tends to buckle into a helix in horizontal boreholes. This causes additional frictional drag resisting the transmission of axial load (resulting from surface slack‐off force) to the bit. As the magnitude of the frictional drag increases, a buckled pipe may become ‘locked‐up’ making it almost impossible to drill further. In case of packers, the frictional drag may inhibit the transmission of set‐up load to the packer. A prior knowledge of the magnitude of frictional drag for a given axial load and radial clearance can help avoid lock‐up conditions and costly failure of the tubular. In this study a neural network model, for the prediction of frictional drag and axial load transmission in horizontal wellbores, is presented. Several neural network architectures were designed and tested to obtain the most accurate prediction. After cross‐validation of the Back Propagation Neural Network (BPNN) algorithm, a two‐hidden layer model was chosen for simultaneous prediction of frictional drag and axial load transmission. A comparison of results obtained from BPNN and General Regression Neural Network (GRNN) algorithms is also presented. Copyright © 2002 John Wiley & Sons, Ltd.     

基于GA的混凝土坝抗震可靠度预测的神经网络模型

针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的混凝土坝抗震可靠度预测的神经网络模型。该模型分别对混凝土坝抗滑稳定可靠度、抗压可靠度和抗拉可靠度进行了预测,并与BP神经网络预测结果进行比较。结果表明,遗传神经网络模型可靠,预测精度高,在岩土工程中利用该方法进行可靠性问题预测是有效及可行的。     

Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.     

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

An objective prediction model for typhoon rainstorm using particle swarm optimization: neural network ensemble

A nonlinear ensemble prediction model for typhoon rainstorm has been developed based on particle swarm optimization-neural network (PSO-NN). In this model, PSO algorithm is employed for optimizing the network structure and initial weight of the NN with creating multiple ensemble members. The model input of the ensemble member is the high correlated grid point factors selected from the rainfall forecast field of Japan Meteorological Agency numerical prediction products using the stepwise regression method, and the model output is the future 24 h rainfall forecast of the 89 stations. Results show that the objective prediction model is more accurate than the numerical prediction model which is directly interpolated into the stations, so it can better been implemented for the interpretation and application of numerical prediction products, indicating a potentially better operational weather prediction.