Exploring the stemflow dynamics and driving factors at both inter- and intra-event scales in a typical subtropical deciduous forest     

Haixia Zhang  Congsheng Fu  Aimin Liao  Can Zhang  Jiufu Liu  Niu Wang  Bin He 《水文研究》2021,35(3):e14091

Numerous efforts have been made to understand stemflow dynamics under different types of vegetation at the inter-event scale, but few studies have explored the stemflow characteristics and corresponding influencing factors at the intra-event scale. An in-depth investigation of the inter- and intra-event dynamics of stemflow is important for understanding the ecohydrological processes in forest ecosystems. In this study, stemflow volume (F_V), stemflow funnelling ratio (F_R), and stemflow ratio (F_%) from Quercus acutissima and Broussonetia papyrifera trees were measured at both inter- and intra-event scales in a subtropical deciduous forest, and the driving factors, including tree species and meteorological factors were further explored. Specifically, the F_V, F_R and F_% of Q. acutissima (52.3 L, 47.2, 9.6%) were lower than those of B. papyrifera (85.1 L, 91.2, 12.4%). The effect of tree species on F_V and F_% was more obvious under low intensity rainfall types. At the inter-event scale, F_V had a strong positive linear correlation with rainfall amount (G_P) and event duration (D_E) for both tree species, whereas F_R and F_% had a positive logarithmic correlation with G_P and D_E only under high-intensity, short-duration rainfall type. F_R and F_% were mainly affected by wind speed and the maximum 30-min rainfall intensity under low-intensity, long-duration rainfall type. At the intra-event scale, for both tree species, the mean lag time between the start of rainfall and stemflow was the shortest under high-intensity, short-duration rainfall type, while the mean duration and amount of stemflow after rain cessation were the greatest under high-amount, long-duration rainfall type. The relationship between stemflow intensity and rainfall intensity at the 5-min interval scale also depended greatly on rainfall type. These findings can help clarify stemflow dynamics and driving factors at both inter- and intra-event scales, and also provide abundant data and parameters for ecohydrological simulations in subtropical forests.  相似文献   

一种基于M估计的Robust-ELM滑坡位移预测方法     

高彩云  高宁 《大地测量与地球动力学》2019,39(2):153-157

采用传统ELM算法进行滑坡位移预测时，其网络输出权值由最小二乘估计得出，导致ELM抗差能力较差，从而造成网络训练参数不准确。为此，将M估计与ELM相结合，提出一种基于M估计的Robust-ELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值，以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例，将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明，该方法能够有效降低粗差对预测的影响，具有良好的抗差能力。  相似文献   

波段选择协同表达高光谱异常探测算法     

朱德辉  杜博  张良培 《遥感学报》2020,24(4):427-438

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。  相似文献   

2019年四川长宁6.0级地震序列时空演化特征及其地震构造环境研究     

宫悦  王宇玺  梁明剑  龙锋  赵敏 《地震》2020,40(4):90-102

地震序列类型能够直观地反映构造应力场环境、 地震构造及孕震环境介质的差异性。 主—余型地震多发生在相对均匀的介质环境, 而前—主—余型地震及震群型地震往往发生在复杂的构造环境。 2019年6月17日四川省宜宾市长宁县发生的6.0级地震余震活动总体呈NW向分布, 其南东段的余震呈相对单一的NW向条带状, 而北西段的余震活动呈现丛集的特征。 两段的地震序列类型也表现出明显的差异性, 南东段地震序列为主—余型, 序列衰减速度较快; 北西段地震序列为震群型, 序列初期衰减非常缓慢, 余震丰富。 此次6.0级地震序列发生在长宁—双河大背斜上, 该复式背斜主要由多个次级褶皱构造和不同走向的伴生断层组成, 结构复杂。 长宁6.0级地震序列的分段性特征表明, 该地震序列不是发生在单一的断裂构造上, 为多个构造级联破裂所导致的复合型地震序列。 此外, 序列北西段余震的深度逐渐变深, 可能预示着该区域受华蓥山断裂带活动的影响, 地震序列对华蓥山断裂带未来地震活动的影响应引起关注。  相似文献   

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

《地学前缘(英文版)》2020,11(3):871-883

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.  相似文献   

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   

基于树轮宽度重建川西南137年秋冬季平均气温变化     

谢成晟  李景吉  高苑苑  石松林  彭培好  杨兴  冯玮娜 《第四纪研究》2020,(1):252-263

在全球气候变暖背景下,青藏高原东南缘的川滇横断山高海拔地区秋冬季温度变化已经成为区域气候变化研究热点。为了更好地了解长时间尺度下秋冬季平均气温变化对树木生长的影响,本文运用泸沽湖地区丽江云杉(Picea likiangensis)树轮宽度资料,建立了标准年表。并基于气温与树轮宽度指数的关系,重建了过去137年来川西南地区的秋冬季平均气温波动历史。重建序列存在2个暖期(1911~1927 A.D.,1992~2015 A.D.)、1个冷期(1939~1991 A.D.)。与其他树轮序列、沉积记录及历史记录的比较和空间相关分析,显示重建结果可靠,且具有区域代表性。集合经验模态(EEMD)分解得到2 a、19 a和54 a的周期控制序列冷暖波动。厄尔尼诺-南方涛动(ENSO),太阳黑子,太平洋年代际涛动(PDO)和北大西洋涛动(NAO)可能是以上周期的驱动因子.  相似文献   

New formulae for capacity energy-based assessment of liquefaction triggering     

Yasaman Jafariavval 《Marine Georesources & Geotechnology》2020,38(2):214-222

Abstract

Liquefaction of loose saturated soil deposits is a hazardous type of ground failure occurring under earthquake excitations. Therefore, an accurate estimation of liquefaction potential is extremely important in geotechnical engineering. In the current study, a new model is proposed which estimates the level of strain energy needed for liquefaction initiation. A compiled database containing cyclic tests gathered from previously published works was used to develop new models to predict liquefaction potential. M5′ algorithm was used to find the best correlation between parameters. It was shown that not only the derived formulas are acceptably accurate but also they feature a very simple structure in comparison with available formulas in the literature. The proposed equations are accurate, physically sound and uncomplicated. Furthermore, safety factors were given for different levels of risk, which can be useful for engineering practice. In addition, the influence of different predictors on the liquefaction potential was evaluated and also the significance of input variables was assessed via sensitivity analysis. Finally, a new model was introduced for preliminary estimation of liquefaction potential.  相似文献   

基于镁铁-超镁铁岩中单斜辉石主量元素含量的决策树集成算法对比     

孙建鹍  杜雪亮  章宝月  王龙  金维浚  张旗  罗熊  朱月琴 《地质通报》2019,38(12):1981-1991

依靠岩浆构造环境的地球化学成分认识岩浆形成过程是岩石地球化学中的重要应用。当前利用岩石地球化学成分判别构造环境的工作还不够深入。用4种基于决策树的机器学习方法对来自全球新生代洋岛玄武岩(OIB)、岛弧玄武岩(IAB)及大洋中脊玄武岩(MORB)等镁铁-超镁铁岩中单斜辉石的13种主量元素构成数据集进行了岩浆构造环境判别和主要特征排序。通过对比4种基于决策树的机器学习方法,验证了树类算法对于地球化学成分识别问题的有效性,并总结出4种方法在处理岩浆构造环境判别问题时的优劣:决策树算法判别过程更易于理解,但是其准确率欠佳;boosting算法中的AdaBoost和GBDT对于岩浆构造环境的鉴别准确度较高,但构造过程复杂;bagging集成算法随机森林在权衡性能和模型可理解性时不失为一个良好的选择。此外,还通过4种算法的特征重要性排序得出Cr_2O_3,TFeO,TiO_2,FeO和Al_2O_3是进行岩浆构造环境判别的重要成分。  相似文献   

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data     

Xiaoyu Sun  Xia Peng  Yiran Chen  Yu Liu 《International Journal of Digital Earth》2019,12(6):661-678

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.  相似文献