Geospatial data mining for digital raster mapping     

Bruce K. Wylie  Neal J. Pastick  Joshua J. Picotte  Carol A. Deering 《地理信息系统科学与遥感》2019,56(3):406-429

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.  相似文献   

毛蚶天津群体形态性状对体质量的影响研究     

张超  李永仁  郭永军  梁健 《海洋通报》2019,38(4):400-404

为探讨形态性状对体质量的影响,指导毛蚶的选育,以毛蚶天津群体为研究对象,测量壳长、壳宽、壳高、体质量,并进行统计分析。结果表明,壳宽是体质量的主要影响因子,其与体质量的相关系数为0.953,直接作用为0.505,间接作用为0.448,壳长、壳高间接作用分别为0.483、0.480,综合决定系数为0.706 6。毛蚶体质量多元回归方程为Y=-40.8+0.438X1+1.124X2+0.469X3,R2=0.924;以壳宽为自变量,体质量回归方程为:Y=5.443×10-3X22.519,R2=0.937。  相似文献   

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations     

Qiujing Pan  Xingru Qu  Leilei Liu  Daniel Dias 《国际地质力学数值与分析法杂志》2020,44(6):874-889

Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.  相似文献   

基于共轭梯度搜索的病态问题处理方法     

刘杰  张娟娟 《大地测量与地球动力学》2019,39(8):863-868

在最小二乘平差准则基础上，把病态平差问题转化为无约束的二次规划问题，并利用优化理论分析病态对平差解的影响。通过共轭梯度搜索算法在可行域中寻找最优步长因子，自动寻找最速下降方向，并给出迭代初值的设置方法。分析近似计算中病态问题与局部最优解的关系，讨论局部最优解的快速迭代方法，并通过实例验证算法的有效性，计算迭代的速度。由于整个过程没有对法方程系数矩阵进行求逆计算，该算法可用于处理大规模系数矩阵高病态的平差问题。  相似文献   

Assessing regression‐based statistical approaches for downscaling precipitation over North America          下载免费PDF全文

Jie Chen  François P. Brissette  Robert Leconte 《水文研究》2014,28(9):3482-3504

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

不同天气现象下毕节高温特征及预报回归检验分析          下载免费PDF全文

吴姗  姚浪  刘健平  帅龙  聂祥 《中低纬山地气象》2022,46(1):68-72

利用毕节2010-2019年观测资料,分析不同天气现象下日最高气温特征,建立高温模型,并对近5 a 24 h高温进行检验,得出如下结论:(1)毕节高温日变化在夏季最稳定,春季波动最大。气温日较差晴天最大,阴天最小,多云时略大于阴间多云。(2)毕节8~10成云出现频率高达65.7%,夏季晴天频率波动大,春、夏季多云频率较高,且按天气现象分类统计月平均高温时,其峰值均出现在7月。(3) 24 h高温预报准确率月、季变化特征明显,夏季准确率最高,较最低的冬季高出21.4%,在区别天气现象的情况下,阴雨天时预报准确率最高,多云时最低,其中12月多云时最低为25%。(4)回归模型分析发现不同季节同种天气现象24 h高温预报影响因子权重差异明显,日照时数和平均本站气压对模型影响程度较高。不同季节晴天影响因子差异最大,拟合效果最好时段在夏季,平均估计误差为1.2℃,估计误差最大在冬季,平均估计误差为1.7℃。  相似文献   

基于逻辑回归的四川青川县区域滑坡灾害预警模型          下载免费PDF全文

方然可  刘艳辉  苏永超  黄志全 《水文地质工程地质》2021,48(1):181-187

四川省青川县滑坡灾害群发,点多面广,区域滑坡灾害预警是有效防灾减灾的重要手段,预警模型是成功预警的核心。由于研究区滑坡诱发机理复杂、调查监测大数据及分析方法不足等原因,传统区域地质灾害预警模型存在预警精度有限、精细化不足等问题。文章在青川县地质灾害调查监测和降水监测成果集成整理与数据清洗基础上,构建了青川县区域滑坡灾害训练样本集,样本集包括地质环境、降雨等27个输入特征属性和1个输出特征属性,涵盖了青川县近9年（2010—2018年）全部样本,数量达1 826个（其中,正样本613个,负样本1 213个）。基于逻辑回归算法,对样本集进行5折交叉验证学习训练,采用贝叶斯优化算法进行模型优化,采用精确度、ROC曲线和AUC值等指标校验模型准确度和模型泛化能力。其中,ROC曲线也称为“受试者工作特征”曲线;AUC值表示ROC曲线下的面积。校验结果显示,基于逻辑回归算法的模型训练结果准确率和泛化能力均较好（准确率94.3%,AUC为0.980）。开展区域滑坡实际预警时,按训练样本特征属性格式,输入研究区各预警单元27个特征属性,调用预先学习训练好的模型,输出滑坡灾害发生概率,根据输出概率分段确定滑坡灾害预警等级。当输出概率P≥40%且P<60%时,发布黄色预警;当输出概率P≥60%且P<80%时,发布橙色预警;当输出概率P≥80%时,发布红色预警。  相似文献   

矿石体重回归分析在新疆乌宗布拉克矿区的应用研究          下载免费PDF全文

毛红伟 《地质找矿论丛》2021,36(2):228-232

在资源储量估算的过程中,矿石小体重的准确与否将直接影响到资源储量估算的客观程度。对矿石小体重的准确预测将需要通过大量的统计分析工作,传统的方法是利用矿石小体重的算术平均值进行估算矿床资源储量,并没有考虑到多矿种对矿石小体重的影响程度。本文利用Excel软件"数据分析"的"回归"功能模块,对实验室所测定的矿石小体重值与其对应品位进行二元线性回归分析,快速、准确地构建矿石体重与其品位的数学模型,从而为资源储量估算提供了更客观、更科学的矿石体重模型。  相似文献   

基于多元线性回归分析的页岩气有利区评价新方法——以上扬子地区五峰组—龙马溪组海相页岩为例          下载免费PDF全文

周业鑫  赵安坤  余谦  张娣  张茜  雷子慧 《沉积与特提斯地质》2021,41(3):387-397

本文以四川盆地及其周缘五峰组-龙马溪组页岩气评价为例,总结了页岩气有利区优选的储层参数、保存参数和压力系数等参数,量化了各关键参数平面分布,运用多元线性回归分析方法计算了参数权重,通过多参数加权叠加公式建立了多元线性回归模型：有利区=-0.317+0.75×埋深+0.285×TOC+0.148×R_O+0.093×孔隙度-0.128×渗透率+0.201×脆性矿物-0.199×压力系数,并开展了定量化优选评价页岩气有利区工作。分析结果表明,各参数权重由大到小排序为：埋深、TOC、脆性矿物和压力系数、R_O、孔隙度、渗透率,代表对含气性的影响作用依次减小。经过量化优选的有利区主要集中于四川盆地内,包括威远-自贡一线、宜宾-泸州-涪陵一线和川东区域为主,盆外的昭通、金沙、道真和黔江区域为相对有利区。该预测评价结果与四川盆地页岩气勘探实际基本相符,有利区表现形式较传统综合地质方法更清晰、直观。  相似文献   

基于偏最小二乘回归的区域换式风速预报订正技术研究     

杨程  姜瑜君  余贞寿  姜文东  康丽莉  王丽吉 《气象》2019,(5):676-684

利用偏最小二乘回归技术,将预报产品作为自变量,相应的自动气象站观测资料作为因变量,对2013-2016年冬季浙江省中尺度区域模式预报近地面风速进行订正和评估。所选956站中多数站点风速订正后有所改善,通过定量分析可知浙江西部地区整体改善效果最好,其中效果明显站点占91.7%;中部地区改善效果明显站点占86.5%;东部沿海地区改善效果略差,明显改善的站点占67%。各地级市整体表现均不错,除舟山地区为49.9%外,其他地区风速改善比例均超过50%。选取2017年1月20日浙江东北地区沿海大风过程分析发现订正后的风速与观测风速更为接近,在定海大岛站点(靠里)中表现尤为明显,订正后的结果具有显著参考价值。  相似文献