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David Medyckyj-Scott Mike Cuthbertson Ian Newman 《International journal of geographical information science》2013,27(1):65-84
Abstract The existence of global computer networks in conjunction with various computer based tools offers the GIS community the possibility of identifying existing spatial data in a faster and more complete way. Using such tools could help the GIS community reduce the high costs of data collection. This paper focuses on the discovery of environmental data using metadatabases and network information resource tools and includes comments on some of the limitations of these computer based tools. The article will conclude by describing how the system developed by the GENIE project attempts to overcome some of these limitations. 相似文献
554.
J. L. Silván‐Cárdenas L. Wang F. B. Zhan 《International journal of geographical information science》2013,27(3):295-318
The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network‐based data model was investigated for representing geographical objects with scale‐induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi‐resolution, sub‐pixel maps that are typically derived from remote sensing techniques. 相似文献
555.
基于ACCRBF网络的多层砖房震害预测 总被引:1,自引:1,他引:0
针对传统震害预测方法逐栋抽样计算建筑物抗震性能的不足,本文提出了一种基于蚁群聚类径向基(ACCRBF)网络模型的建筑物震害预测方法。依据不同地震动峰值加速度下多层砖房的实际震害资料,对模型进行训练,在模型的输入和输出之间建立映射关系,并利用这种映射关系对未知样本进行分类,实现对多层砖房的震害分析和预测。模型的输入为反映结构的震害影响因子,输出为给定的地震动峰值加速度下结构震害等级。研究表明,基于ACCRBF网络模型的多层砖房震害预测结果与震害实例基本吻合,具有推广应用价值。 相似文献
556.
Bimlesh Kumar 《水文科学杂志》2013,58(5):956-966
Abstract Bed material load, which comprises bed load and suspended load, has been extensively studied in the past few decades and many equations have been developed, but they differ from each other in derivation and form. If a process can be related to various flow conditions on a general basis, a proper understanding of bed material load movement can be ascertained. As the process is extremely complex, obtaining a deterministic or analytical form of it is too difficult. Neural network modelling, which is particularly useful in modelling processes about which knowledge of the physics is limited, is presented here as a complimentary tool for modelling bed material load transport. The developed model demonstrated a superior performance compared to other traditional methods based on different statistical criteria, such as the coefficient of determination, Nash-Sutcliffe coefficient and discrepancy ratio. The significance of the different input parameters has been analysed in the present work to understand the influence of these parameters on the transport process. Editor D. Koutsoyiannis Citation Kumar, B., 2012. Neural network prediction of bed material load transport. Hydrological Sciences Journal, 57 (5), 956–966. 相似文献
557.
同一地名映射不同物理空间位置的地名歧义现象普遍存在,地名消歧是指为地名分配唯一地理位置的过程.欧美国家的地名数据库和相关知识库比较完善,提出一系列通用或适宜个别语种的地名消歧方法.由于我国当前缺乏类似的高质量数据资源,难以直接引入这些方法解决中文地名消歧问题.该文提出在地名识别和地名匹配的基础上,通过构建歧义地名行政隶属关系树状图,利用上下文相关地名在树状图中出现的子节点数,判断歧义地名指向的地理位置.实验表明,该方法简单易行,而且可以达到较好的消歧效果. 相似文献
558.
3个时期骆马湖大型水生植物的分布及变化 总被引:2,自引:0,他引:2
大型水生植物对湖泊的生物地球化学循环具有重要影响.一方面,大型水生植物在生长过程吸收营养;另一方面,其通过向水体释放氧气而影响磷元素以及其他相关因子,进而影响磷元素的生物地球化学循环.为了从宏观上了解骆马湖生态系统变化,以1990年9月20日、2000年5月2日和2008年10月15日Landsat TM/ETM+影像为主要数据源,以大型水生植物的归一化植被指数(NDVI)为测试变量,运用分类回归树(classifica-tion and regression tree,CART)方法确定分割阈值,通过构建知识决策树的方法识别骆马湖大型水生植物动态变化特征.3个时期的遥感分类的总体精度与kappa系数分别为92.28%和0.87、91.73%和0.86、93.38%和0.88,因此,该方法的分类精度完全满足骆马湖水生植物分布及变化的研究.研究结果表明,骆马湖大型水生植物分布面积由1990年的55.461 6 km2,减少到2000年的41.801 4 km2,2008年又增加到79.065 km2;大型水生植物主要分布在骆马湖北部河湖交汇区;人类活动干扰是造成骆马湖大型水生植物分布面积发生变化的主要原因. 相似文献
559.
ABSTRACTThroughfall drop size distributions (DSDs) are important for plant–soil interactions. This is the first known study to quantify differences in throughfall DSDs with the presence and absence of foliage. Employing a disdrometer, three parameters solely representing throughfall drip were measured and calculated: maximum drop diameter (DMAX), median volume diameter of drops (D50DR) and relative volume percentage of drops (pDR). Beneath Liriodendron tulipifera L. in Maryland (USA), DMAX, D50DR and pDR were substantially larger when the canopy was unfoliated. In fact, the presence or absence of foliage was one of the primary factors affecting all three throughfall DSDs along with air temperature, according to the boosted regression tree analysis. Experimental results were attributed to differing physical properties of intercepted water between foliated and unfoliated periods and differential water behavior on leaves and bark. Future work should examine the effects of concentrated drip points on the development of throughfall-induced hot spots.
Editor M.C. Acreman; Associate editor F. Hattermann 相似文献
560.
AbstractThe accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time. 相似文献