基于随机森林模型的农业电子商务浅析——洛川苹果网络销售问题与对策     

张晓涵  马敏飞  杨联安 《地下水》2020,(1):119-122

随着互联网产业的飞速发展,电子商务开始进入农业领域。以电子商务起步较早的"洛川苹果"作为研究对象进行调研。基于随机森林模型的决策树集成算法,对农业网络销售体系整体进行数据分析,模型构建,从问题表象出发挖掘其在不同部分的影响因子,最终基于影响因素解决问题,提出合理化建议:加强农村基础设施建设、健全农村公共服务体系以及完善农村电子商务培训制度等,因地制宜,推进农业电子商务的健康发展。  相似文献   

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process     

《地学前缘(英文版)》2020,11(6):2207-2219

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

考虑填充墙影响的钢筋混凝土厂房结构地震易损性分析     

梁志强  徐超  温增平 《震灾防御技术》2019,14(4):707-718

采用对角斜撑模拟纵向填充墙的作用，建立考虑填充墙和不考虑填充墙厂房结构模型，采用拉丁超立方抽样技术建立考虑材料不确定性的结构分析样本，基于随机Pushover分析确定结构不同破坏状态下的统计参数。综合考虑结构材料强度及输入地震动不确定性的影响，通过非线性时程分析开展单层钢筋混凝土厂房结构易损性研究，在此基础上比较结构横、纵向易损性的差异，研究填充墙对结构易损性的影响。研究结果表明:钢筋混凝土厂房结构体系横向地震易损性显著大于纵向地震易损性；对纵向结构体系而言，加入填充墙会明显降低结构易损性，但在相同强度的地震动作用下，填充墙破坏程度比主体结构严重，这与厂房结构的实际震害特征相符。  相似文献   

基于最大球算法定量表征土体空间孔隙网络     

解朝阳  张巍  姚东方  王坚  朱雨辰 《工程地质学报》2020,28(1):60-68

孔隙网络控制着土体渗流、排水固结与基质吸力等重要工程性质。本文介绍了多孔介质孔隙网络最大球建模基本原理与算法。以显微CT扫描振捣干法生成的南京粉砂试样为例,采用最大球算法建立了试样三维重构模型表征单元体(REV)的空间孔隙网络球棍模型,计算得到了样品REV尺度的孔隙网络参数,统计发现,孔隙半径、喉道半径、孔隙配位数、孔隙截面形状因子、喉道截面形状因子与喉道长度等孔隙参数均近似服从正态分布,孔隙体积近似服从衰减型指数分布。孔隙与喉道半径分别分布在100μm与65μm以内,两者数学期望分别为40. 0μm与18. 0μm;配位数分布在25以内,数学期望为5. 1;孔隙与喉道截面形状因子分别分布在0. 01~0. 04与0. 01~0. 05的区间内,两者数学期望分别为0. 019与0. 033;喉道长度分布在100~800μm以内,数学期望为292. 22μm。同时发现,样品中体积小于1. 5×10^7μm^3的小孔隙数量超过90%。本方法可应用于土体细观孔隙结构的定量表征。  相似文献   

非等间距GM(1,1)模型的总体最小二乘算法及其病态问题     

陶武勇  花向红  鲁铁定  陈西江  张伟 《大地测量与地球动力学》2019,39(1):45-50

在非等间距GM(1,1)模型中，系数矩阵中有无误差的常数项和有误差的随机项，并且系数矩阵与观测向量误差同源，即系数矩阵与观测向量中有相同的元素存在，这些相同元素应该有相同的改正数，为此本文推导了一种适合非等间距GM(1,1)模型求解的总体最小二乘算法。同时，考虑到非等间距GM(1,1)模型中存在病态问题时影响总体最小二乘计算结果的稳定性，提出对系数矩阵常数列乘以某一常数的方法，以改善病态问题。  相似文献   

DATABASE OF LANDSLIDES TRIGGERED BY 2015 GORKHA(NEPAL) M_W7.8 EARTHQUAKE     

XU Chong  TIAN Ying-ying  SHEN Ling-ling  MA Si-yuan  XU Xi-wei  ZHOU Ben-gang  HUANG Xue-qiang  MA Jun-xue  CHEN Xi 《地震地质》2018,40(5):1115-1128

In this study, a detailed database of landslides triggered by the 25 April 2015 Gorkha (Nepal)M_W7.8 earthquake is constructed based on visual interpretation of pre- and post-earthquake high-resolution satellite images and field reconnaissance. Results show the earthquake triggered at least 47 200 landslides, which have a NWW direction spatial distribution, similar with the location and strike of the seismogenic fault. The landslides are of a total area about 110km² and an oval distribution area about 35 700km². On the basis of a scale relationship between landslide area (A)and volume (V), V=1.314 7×A^{1.208 5}, the total volume of the coseismic landslides is estimated to be about 9.64×10⁸m³. In the oval landslide distribution area, the landslide number density, area density, and volume density were calculated and the results are 1.32km^-2, 0.31%, and 0.027m, respectively. This study provides a detailed and objective inventory of landslides triggered by the Gorkha earthquake, which provides very important and essential basic data for study of mechanics of coseismic landslides, spatial pattern, distribution law, and hazard assessment. In addition, the landslide database related to an individual earthquake also provides an important earthquake case in a subduction zone for studying landslides related to multiple earthquakes from a global perspective.  相似文献   

基于FFT-MA的随机介质建模方法          下载免费PDF全文

杨修伟  朱培民  毛宁波  徐志伟  肖丹 《地球物理学报》2018,61(12):5007-5018

随机介质是描述地球介质小尺度非均匀性的有效模型，在地震散射波场分析、储层描述等领域具有广泛应用，快速准确的随机介质建模方法是开展相关研究的基础与前提.本文首次将FFT-MA（Fast Fourier Transform Moving Average）算法引入到随机介质建模研究中，分析了该方法与传统随机介质建模方法相比具有的优势，并提出了基于FFT-MA的非平稳随机介质建模方法.建模实验表明，与传统的基于谱分解定理的随机介质建模方法相比，基于FFT-MA的方法在空间域产生随机数序列，使随机数序列与结构参数分离，因此，随机数序列与所建模型存在空间上的对应关系，可以分区域建模和局部修改模型.在非平稳随机介质模型建模时，滑动窗口的大小可以根据自相关长度变化而变化，避免了每个采样点都建立一次完整大小的模型，提高了建模效率.因此，FFT-MA随机介质建模方法能准确构建满足自相关函数要求的平稳及非平稳随机介质模型，具有建模效率高、灵活、实用的优点.  相似文献   

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning     

《International Journal of Applied Earth Observation and Geoinformation》2020

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.  相似文献   

BDS-3多频点伪距单点定位性能研究     

张乾坤  刘小生  何琦敏 《测绘通报》2020,(1):71-75

针对目前有关北斗三号系统伪距单点定位研究较少，未对三号系统多个频点的定位性能进行对比分析的现状，本文利用实测数据对三号系统多个频点的定位性能进行了研究，并联合BDS-2、Galileo进行了同频伪距单点定位试验，统计分析了组合定位多个频点的同频定位结果。试验结果表明：目前三号系统单独定位能力有限，不适合单独定位；BDS-3/Galileo同频组合定位可以弥补BDS-3新频点单频定位时卫星个数不足、数据不完整导致的定位精度过差的情况，同时能够提高Galileo的定位精度；BDS-2/3的B3I频点与BDS/Galileo组合的B2b频点的定位精度均与GPS的L1频点的定位精度相当。  相似文献   

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献