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1.
研究基于RNN、LSTM、GRU深度学习模型,针对NOAA浮标数据集中的44013、44014、44017浮标的数据,通过斯皮尔曼相关性分析提高模型预测效果。实验结果表明,在进行相关性分析后,S-RNN、S-LSTM、 S-GRU的预测效果均比原始RNN、LSTM、GRU模型预测效果好。此外,提出一种基于LSTM的LSTM-Attention 波高预测模型,并进行相关实验,量化LSTM-Attention模型的预测效果,实验结果表明LSTM-Attention模型有更好的预测效果。为评估模型的泛化能力,研究还提出了一种采用邻近浮标数据进行学习,预测浮标缺失数据的方 法。实验结果表明,该方法的预测精度可以达到97.93%。本研究为海浪预测提供了新的方法和思路,也为未来深 度学习模型在海浪预测中的应用提供了参考。 相似文献
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Farmers along the Amazon River each year face multiple natural hazards that threaten crop production and limit the potential for agricultural development of the expansive floodplain and active channel. In this paper we report the findings of a study of natural hazard-related risk associated with rice production on silt bars in the active channel of the Amazon River near Iquitos, Peru. Data were gathered in four rice producing communities in 2014 using household surveys (n = 83 households), focus group discussions, surveying of land elevations along the Amazon River, and interpretation of remote sensing imagery. The probability, extent, and severity of rice crop shortfalls were estimated for recent production years and the economic losses to farming households were also assessed. Our findings point to a very high risk of crop shortfalls due to natural hazards, suggesting that a good year brings rice farmers bounty and a bad year, near penury. River stage reversals (repiquetes) and edaphic conditions were found to be more problematic than the often cited hazard of high and/or early floods. Also surprisingly, farmers’ perceptions of hazards and risk diverged markedly from actual shortfalls experienced during the production years studied. Our results provide the first quantitative estimates of risk due to the multiple natural hazards along the Amazon River and point to the need to assist lowland farmers with risk mitigation so as to unlock the considerable potential of Amazon floodlands for agricultural production. 相似文献
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这是获得江苏省软科学课题奖的省级项目,以测绘期刊为例,选择江苏省测绘学会主办的《现代测绘》为研究平台,选择7所高校教学研究机构和10个不同行业、不同等级的测绘生产单位作为调查研究的对象,力求能够全面反映测绘学科科技期刊对行业落实科学发展观的现实作用以及下一步改进工作的重点. 相似文献
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The existing crisis management research mostly reveals the patterns of the public's panic levels from the perspectives of public management, sociology, and psychology, only a few studies have revealed the spatiotemporal characteristics. Therefore, this study investigates the spatial distribution and temporal patterns and influencing factors on the general public's panic levels using the Baidu Index data from a geographic perspective. The results show that: (1) The public's panic levels were significantly correlated with the spatial distance between the epicenter and the region of investigation, and with the number of confirmed cases in different regions when the pandemic began to spread. (2) Based on the spatial distance between the epicenter and the region, the public's panic levels in different regions could be divided into three segments: core segment (0–500 km), buffer segment (500–1300 km), and peripheral segment (>1300 km). The panic levels of different people in the three segments were consistent with the Psychological Typhoon Eye Effect and the Ripple Effect can be detected in the buffer segment. (3) The public's panic levels were strongly correlated with whether the spread of the infectious disease crisis occurred and how long it lasted. It is suggested that crisis information management in the future needs to pay more attention to the spatial division of control measures. The type of crisis information released to the general public should depend on the spatial relationship associated with the place where the crisis breaks out. 相似文献
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Mehmet ?ahin B. Yi?it Y?ld?z Ozan ?enkal Vedat Pe?temalc? 《Journal of the Indian Society of Remote Sensing》2012,40(3):399-409
This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LSTANN) and Becker and Li temperature(LSTB-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively. 相似文献
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GPS精密定位中的海潮位移改正 总被引:2,自引:0,他引:2
根据海洋负荷潮理论,利用NAO99b全球海潮模型,计算了中国部分IGS站的海潮位移改正,并将海潮位移改正应用到GPS数据处理当中。在GAMIT软件的解算过程中,分别按加入和不加入海潮位移改正,对GPS基线分量和测站坐标分别进行了计算和比较分析。结果表明,海潮位移改正无论是对GPS基线分量还是对测站坐标,都有一定的影响。 相似文献