A shift towards wetter and windier conditions in southern Sweden around the prominent solar minimum 2750 cal a BP          下载免费PDF全文

ANETTE MELLSTRÖM  NATHALIE VAN DER PUTTEN  RAIMUND MUSCHELER  RIXT DE JONG  SVANTE BJÖRCK 《第四纪科学杂志》2015,30(3):235-244

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Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method          下载免费PDF全文

Lei Ye  Jianzhong Zhou  Hoshin V. Gupta  Hairong Zhang  Xiaofan Zeng  Lu Chen 《水文研究》2016,30(15):2703-2716

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

A network theory approach for a better understanding of overland flow connectivity          下载免费PDF全文

Rens J. H. Masselink  Tobias Heckmann  Arnaud J. A. M. Temme  Niels S. Anders  Harm P. A. Gooren  Saskia D. Keesstra 《水文研究》2017,31(1):207-220

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub‐) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for example, habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognized within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterize and quantify overland flow connectivity dynamics on hillslopes in a humid sub‐Mediterranean environment by using a combination of high‐resolution digital‐terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi‐natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely because of soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

一种新的温度传感器湿度补偿方法     

邹水平  行鸿彦  于祥 《南京气象学院学报》2016,8(6):533-539

针对温度传感器测量中易受湿度影响的问题,通过对思维进化算法（Mind Evolutionary Algorithm,MEA）中的趋同操作、异化操作及收敛条件进行研究改进,对趋同操作中的散布权值进行自适应调整,在异化操作中引入差分进化算法的变异操作,并考虑收敛条件中搜索平面平缓的情况,提出了基于改进思维进化算法的BP神经网络湿度补偿方法.由湿度影响检定实验得到的样本数据,利用此补偿方法建立湿度补偿模型,将补偿结果与未经优化的BP神经网络模型的结果进行比较研究.结果表明,基于改进思维进化算法的BP神经网络模型补偿精度较高,收敛速度快,计算量小,可有效提高温度传感器的测量精度和可靠性,便于实际应用.  相似文献   

顾及邻域居民地群组相似性的道路网匹配方法     

王骁  钱海忠  何海威  陈竞男  胡慧明 《测绘学报》2016,45(1):103-111

现有多源同比例尺道路网匹配方法中,大多只利用道路自身特征进行匹配,而较少顾及道路周边要素对匹配过程的影响和约束,从而影响了道路网匹配效果的进一步提高,特别是对系统误差改正后仍存在一定位置或旋转偏差的道路数据进行匹配时,这种影响尤为明显。本文借鉴人类对陌生环境的空间认知特点,提出了一种顾及邻域居民地群组相似性的道路网匹配方法。该方法通过构建城市骨架线网确定与道路相邻的居民地群组,进而计算居民地群组空间关系和几何特征相似度来获得对应道路的匹配结果。其特点在于:对存在位置或旋转偏差的道路数据匹配,以其邻域空间内居民地群组的整体相似性指标来带动道路自身匹配,实际上是增加了周边居民地群组对道路匹配过程的约束,更具鲁棒性。试验及对比分析表明,本方法能够较好地解决系统误差改正后仍存在较大位置和旋转偏差的道路数据间的匹配问题,提高匹配的正确率。  相似文献   

回归-Elman网络在矿坝变形预测中的应用     

王祖顺  韩吉德  王春青 《测绘工程》2016,25(1):39-42

金矿开展矿坝的变形监测工作,引入多层递阶回归分析模型,有较高的预测精度,但方法较繁琐,计算较复杂。由于变形数据可分离成趋势项与随机项,趋势项可用多元线性回归良好地拟合;随机项的预测,文中采用Elman网络建模计算,最后利用矿坝的实测高程位移数据进行验证,并与多层递阶回归进行比较。结果表明:回归-Elman网络模型比多层递阶回归的预测精度更高,效果更好,且方法简洁实用性强。  相似文献   

改进后灰色神经网络及短期基坑沉降预测研究     

夏磊凯  黃其欢  夏晨翔  吴海兵 《测绘工程》2016,25(6):56-60

在短期基坑沉降监测中,由于数据量少且呈非线性变化,沉降模型很难准确建立。灰色GM(1,1)对数据少、趋势性强、波动小的数据有较高的预测精度,但不能模拟复杂的非线性函数;BP神经网络可以对非线性数据进行学习训练,具有自学习、自适应能力;通过将GM(1,1)与BP神经网络组合,并优化网络部分的学习率、权值和阈值等,建立一种改进的灰色神经网络模型,该模型具有对非线性数据自学习、自适应能力和预测精度更高等优点。通过某基坑沉降监测分析,验证改进的灰色神经网络模型预测精度更高,适合短期建模,具有很好的实用性。  相似文献   

一种裸露土壤湿度反演方法   总被引：1，自引：0，他引：1  

张清河  徐飞  朱国强 《测绘科学》2016,41(2):11-14

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。  相似文献   

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran   总被引：1，自引：0，他引：1  

Mehdi Azari  Amin Tayyebi  Marco Helbich  Mohsen Ahadnejad Reveshty 《地理信息系统科学与遥感》2016,53(2):183-205

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.  相似文献   

均生函数与BP神经网络耦合模拟预测模型(MGF-BP-I)的建立与应用   总被引：2，自引：1，他引：1       下载免费PDF全文

刘丹辉  马龙  刘廷玺  杜志军  王静茹  黄星 《水文》2016,36(6):7-15

水文气候因子模拟预测对气候变化研究、农业墒情预报、生态环境改善、水资源合理开发利用等具有一定参考意义。均生函数、BP神经网络及其结合改进方式在模拟预测中各有优点,被广泛应用,但仍有进一步改进空间。针对MGF、MGF-OSR、MGF-OSR-BP等方法粗选因子集、粗选集组合筛选、收敛适应性、精度控制等可改进空间,进一步发挥均生函数和BP神经网络优势,建立了MGF-BP-I模拟预测模型。利用MGF-OSR、MGF-OSR-BP、MGF-BP-I对科尔沁沙地区域平均年降水进行了模拟预测。结果表明,建模期MGF-OSR-BP、MGF-BP-I拟合效果均较好,MGF-BP-I建模阶段最优模式精度优于MGF-OSR-BP,MGF-BP-I整体同时最优模式结果也非常好。检验期,MGF-BP-I检验阶段最优及整体同时最优两种模式拟合效果最好,相比其他模式精度有所提高。MGF-BP-I考虑更加全面,充分发挥了均生函数和BP神经网络优势,精度远高于MGF-OSR和MGF-OSR-BP,MGF-BP-I整体同时最优模式更符合实际应用,效果理想,可用于水文气候因子模拟预测。  相似文献