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

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。     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

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

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.     

1951—2017年热带气旋移动速度特征统计分析

热带气旋移动速度是目前热带气旋研究热点之一。本文利用1951—2017年中国国家气象局最佳路径集资料,系统分析了西北太平洋海域热带气旋不同强度等级移动速度的年代际特征,初步探讨其原因,并对不同强度等级移动速度的分布特征进行系统诊断。结果表明,1951—2017年间平均移动速度(所有强度等级平均及对于大部分强度等级)呈现出减慢趋势,且主要体现在强热带气旋等级移动速度减慢;热带气旋向高纬度迁移趋势理论上将使移动速度增大而移动速度仍然减慢,说明气候因素是引起移动速度减慢的一个重要因素;不同强度等级平均移动速度、增强阶段基本相当,减弱阶段随强度减弱在初、中期明显增加,之后又逐渐减小,变性气旋移动速度显著增加;不同强度等级速度概率分布,增强阶段基本相同且相对集中,减弱阶段差异较大且相对分散,变性气旋最为分散。     

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.     

基于BIM的地震灾后民用建筑重建工程施工成本控制方法

采用传统的成本控制方法,存在质量和成本无法同时优化、成本预测精确度低,成本控制效果不佳等问题,为此,提出基于BIM的建筑重建工程施工成本控制方法。采用灰色系统理论预测建筑施工成本具体面临的问题,以各期施工成本相关信息数据作为模型数据基础;结合BIM技术进行成本控制动态模型构建,在模型中引入可辨识矩阵设定成本各指标的相对重要度,进一步找寻施工成本各项指标间的关系,按其相互关系进行排序,得到子成本与总成本间的规则联系,并就规则制定控制策略,调整成本控制动态模型以实现施工成本的动态控制。实验结果表明,所提方法的成本预测精度始终在75%以上,成本控制结果与实际值的拟合度高达95%,相比当前方法具有更好的控制性能。     

低纬高原雹云识别物元可拓模型及效果检验

雹云识别是一个多参数的模式识别问题,应用物元可拓思想将单参数雹云识别的不相容性转化为相容性问题。在与成都地区雹云识别的物元可拓模型进行对比后,根据低纬高原地形及气候特点,通过适当增加参数,构造完成低纬高原雹云识别的物元可拓模型,以此进行雹云识别,准确率达86％以上。     

基于随机事件集的风暴潮重现期计算的合理性研究

为了研究随机事件集实际应用于计算南通如东岸段风暴增水的合理性问题,基于ADCIRC模型模拟影响南通如东岸段基于随机事件集的风暴增水,然后利用P-Ⅲ型曲线对年增水极值进行拟合,得到各典型重现期下的增水值,并与由28年历史资料拟合得到的各重现期下增水值进行对比。结果表明,在历史资料长度所及的28年以及以下重现期,基于随机事件集模拟得到的重现期增水值与基于历史实测资料的比较符合,说明随机事件集的结果在低重现期情况下结果良好。对于高重现期增水值,基于随机事件集的拟合结果显著大于基于历史资料的拟合结果。由于历史资料时间太短,不足以发生非常极端的风暴潮事件,故对高重现期的拟合结果难以保证可信度,所以不能排除随机事件集拟合结果的合理性。随机事件集在南通如东岸段对低重现期增水估计准确,同时也能较充分地估计高重现期增水,在目前缺乏百年千年时间尺度的实测资料的情况下,不失为一种良好的风暴增水重现期计算工具。在全球变暖情况下,超强台风出现几率大大增加,典型重现期增水值也会相应提高,为了预防风暴灾害的侵袭,需要加深加固海堤、江堤等海岸工程。     

复杂山地随机介质GMM-ULTI法射线追踪

对复杂山地介质的非均质性以及介质中地震波运动学特征进行深入研究,对于提高复杂山地区域地震勘探的效果有着重要的理论意义和实际价值.为了研究复杂山地非均质性和该介质中地震波的一些运动特性,提出了一种复杂山地随机介质的建模方法和一种新的射线追踪算法.与常规算法相比,复杂山地随机介质的生成方法采用更贴近实际介质特点的梯度介质作为背景介质,并在模型生成过程中加入地形修正步骤;新提出的GMM-ULTI射线追踪算法,充分融合群推进法、迎风思想、走时插值法的优势,采用先计算走时后追踪射线路径的两步策略完成射线追踪.算法分析与计算实例表明:复杂山地随机介质的生成方法能灵活、精细且更贴近实际地刻画复杂山地介质的非均质特点;新射线追踪算法兼顾精度和效率、能无条件稳定且灵活地适应复杂山地随机介质的特点;同时基于对几个模型试算结果的分析也得出了复杂山地随机介质中的地震波的一些传播规律.     

多相离散随机介质模型及其探地雷达波场特征研究

沥青混凝土是由骨料、沥青胶浆、空气按照一定的体积百分比混合而成的多相非匀质混合物,其骨料、沥青胶浆和空气的体积不等、形状各异、介电特性不同、空间位置随机分布,具有明显的多相、离散、随机介质特征.本文基于随机介质模型理论,(1)测量与统计了介电常数在典型沥青混凝土芯样空间上的随机分布统计特征;(2)估算了沥青混凝土介质的自相关函数及其特征参数(自相关长度、自相关角度等),确定其随机介质类型;(3)提出了量化约束下的多相离散随机介质建模算法,以混合型椭圆自相关函数为基础,构建了不同粗糙度因子的多相离散随机介质模型;(4)构建了不同空隙率的多相离散随机介质模型,正演模拟与对比分析了探地雷达波在均匀介质、连续型随机介质和多相离散随机介质中的传播特征.结果表明:多相离散随机介质模型不仅描述了沥青混凝土的多相、离散与空间随机分布统计特征,而且进一步描述了其各组成物质体积百分比,能更全面、准确地描述沥青混凝土的介质特征,同时也为描述其他类似材料或介质提供了新的方法和途径;在多相离散随机介质模型中,探地雷达波散射强烈,随机、无序传播的散射波相互叠加干涉,形成了明显的随机扰动和"噪声",致使异常体反射波扭曲变形、不连续,降低了探地雷达回波的信噪比和分辨率.研究探地雷达波的随机扰动特征与多相离散随机介质模型参数之间的关系,将为定量评价多相离散随机介质的属性参数提供参考和帮助.