基于PSR-WSVM模型的边坡位移预测     

李建新  刘小生  肖钢  周文  刘仁志 《大地测量与地球动力学》2020,40(6):577-580

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning     

Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.  相似文献   

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms   总被引：1，自引：0，他引：1  

J. Federico Ramírez  Olac Fuentes  Ravi K. Gulati 《Experimental Astronomy》2001,12(3):163-178

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

Anthropogenic influence on trace element geochemistry of healing mud (peloid) from Makirina Cove (Croatia)     

S. Miko  G. Koch  S. Mesić  M. Šparica-Miko  M. Šparica  R. Čepelak  A. Bačani  P. Vreča  T. Dolenec  S. Bergant 《Environmental Geology》2008,55(3):517-537

Due to their balneotherapeutic features, the organic-rich sediments in Makirina Cove are an important source of healing mud. An environmental geochemistry approach using normalization techniques was applied to evaluate the anthropogenic contribution of trace metals to sediments used as healing mud. Sediment geochemistry was found to be associated with land-use change and storm events, as well as with proximity of a road with heavy traffic in the summer months. Local valley topography preferentially channels lithogenic and pollutant transport to the cove. Concentrations and distribution of trace metals indicate lithogenic (Ni, Cr, Co) and anthropogenic (Pb, Cu, Zn and Se) contributions to the sediments. The calculation of enrichment factors indicates a moderate (EFs between 2–3.5) input of anthropogenic Cu and Pb in surficial sediments to a depth of 10 cm. Patients using the Makirina Cove sediments as healing mud could be to some extent exposed to enhanced uptake of metals from anthropogenic sources via dermal contact.  相似文献   

Experimental study on the deterioration and natural remediation of the Ariake Sea tidal mud caused by the sea laver treatment acid practice and the upward seepage of pore water liquid     

Yan-Jun Du  Song-Yu Liu  Shigenori Hayashi 《Environmental Geology》2008,55(4):889-900

This study presents a laboratory study of the following two aspects: (1) the influence of sea laver treatment acid on the geoenvironmental properties of Ariake Sea tidal mud, and (2) the natural remediation effect on the sea laver treatment acid contaminated Ariake Sea tidal mud caused by the upward seepage of pore water liquid in the mud. Firstly, the mechanisms of the transport of sea laver treatment acid in the Ariake Sea tidal mud and the generation mechanisms of the upward seepage flow in the Ariake Sea tidal mud are discussed. Secondly, a series of one-dimensional laboratory infiltration tests were carried out to investigate the deterioration of the Ariake Sea tidal mud caused by the sea laver acid treatment practice. Test results reveal that the acid treatment practice caused considerable change in the geochemical properties of the mud in terms of increase in sulfide content and decrease in pH value. After the treatment by the sea laver treatment acid, the sulfide content of the mud even exceeded the safe limit value for the benthos, which represents undesirable living condition for benthos. Thirdly, series of laboratory fresh seawater infiltration tests for the deteriorated Iida site mud were conducted to illustrate this natural remediation efficiency. It is found that with the infiltration of the fresh seawater, the sulfide content of the Iida site mud was considerably reduced and pH value increased to an acceptable range for benthos living in the tidal flat mud. With the increase in the infiltration time and the hydraulic gradient, the remediation efficiency could be increased.  相似文献   

旋流除砂机理研究及自动旋流除砂器的开发          下载免费PDF全文

张学强  鄢泰宁 《探矿工程》2008,35(11):34-36

旋流除砂技术是利用离心力从泥浆中分离固相成分的技术,可广泛应用在地质钻探、有色、冶金工业及石油天然气工业领域。研制开发的自动旋流除砂器弥补了传统除砂器固相成分分离效果不佳、排砂口易堵的缺陷,显著提高泥浆或矿浆的净化质量与除砂效率,且能自动排堵,降低了除砂工作的时间消耗和成本。  相似文献   

预防油页岩地层钻进中压差卡钻的措施     

赵宪富  台沐礼  孙德学  张元清  吕洪富 《探矿工程》2008,35(9):21-23

分析了油页岩地层钻进中压差卡钻发生的原因,提出了预防压差卡钻的途径及处理方法,并通过工程实例来强调预防为主的重要性。  相似文献   

深井旋转轴套流场的CFD模拟     

兰伟  曾良才  陈新元  朱瑾 《探矿工程》2008,35(11):20-23

旋转轴套在超深井工作时,其井下流动是三维湍流流动,旋转轴套的旋转和表面曲率效应以及随之而来的哥氏力和离心力,使流场在超深井的流动中极其复杂,同时,由于流体介质泥浆属于液固两相流,更致使内部流场测试困难,而且超深井中的工况在使用常规方法已很难得到较准确的数据。为此,将计算流体力学软件Fluent应用于超深井下流场的模拟,基于Navier-Stokes方程和Reynolds应力方程模型,建立多种仿真模型,在相同条件下,使用CFD仿真软件Fluent模拟仿真的不同结果,优化与旋转轴套叶片设计相关的几何参数,提高  相似文献   

基于AGA的SVM需水预测模型研究   总被引：1，自引：0，他引：1  

张灵  陈晓宏  刘丙军  王兆礼 《水文》2008,28(1):38-42,46

需水预测是一个由城市人口、工业水平、社会经济水平共同作用的多因素、多层次的复杂非线性系统.其结果将直接影响受区域水资源承载力约束的产业结构、布局形态等决策.作为一种集中参数预报方法,支持向量机方法具有对未来样本的较好的泛化性能,对于这类资料缺乏、系统结构尚欠清晰的问题可以取得较好的模拟和预测结果.基于此,本文将支持向量机方法引入需水预测领域,建立了需水预测支持向量机模型.同时,本文将加速遗传算法和支持向量机方法耦合起来,构造了支持向量机模型参数的自适应优化算法.模型在珠海市的应用实例表明:与简单遗传算法比较,AGA的模型参数寻优效率更高;与BP神经网络模型相比,SVM模型较好地解决了小样本、经验性等问题,并取得了较高的预测精度.  相似文献