全文获取类型
收费全文 | 396篇 |
免费 | 67篇 |
国内免费 | 49篇 |
专业分类
安全科学 | 129篇 |
废物处理 | 5篇 |
环保管理 | 37篇 |
综合类 | 211篇 |
基础理论 | 56篇 |
污染及防治 | 5篇 |
评价与监测 | 19篇 |
社会与环境 | 23篇 |
灾害及防治 | 27篇 |
出版年
2023年 | 13篇 |
2022年 | 19篇 |
2021年 | 12篇 |
2020年 | 20篇 |
2019年 | 20篇 |
2018年 | 14篇 |
2017年 | 18篇 |
2016年 | 26篇 |
2015年 | 25篇 |
2014年 | 15篇 |
2013年 | 20篇 |
2012年 | 40篇 |
2011年 | 45篇 |
2010年 | 29篇 |
2009年 | 29篇 |
2008年 | 12篇 |
2007年 | 28篇 |
2006年 | 15篇 |
2005年 | 19篇 |
2004年 | 10篇 |
2003年 | 14篇 |
2002年 | 11篇 |
2001年 | 8篇 |
2000年 | 4篇 |
1999年 | 6篇 |
1998年 | 12篇 |
1997年 | 5篇 |
1996年 | 3篇 |
1995年 | 6篇 |
1994年 | 2篇 |
1993年 | 4篇 |
1992年 | 4篇 |
1991年 | 3篇 |
1989年 | 1篇 |
排序方式: 共有512条查询结果,搜索用时 140 毫秒
31.
32.
基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案 总被引:1,自引:0,他引:1
针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考. 相似文献
33.
PIA E. LENTINI PHILIP GIBBONS JOSIE CARWARDINE JOERN FISCHER MICHAEL DRIELSMA TARA G. MARTIN 《Conservation biology》2013,27(4):796-807
Although the concept of connectivity is decades old, it remains poorly understood and defined, and some argue that habitat quality and area should take precedence in conservation planning instead. However, fragmented landscapes are often characterized by linear features that are inherently connected, such as streams and hedgerows. For these, both representation and connectivity targets may be met with little effect on the cost, area, or quality of the reserve network. We assessed how connectivity approaches affect planning outcomes for linear habitat networks by using the stock‐route network of Australia as a case study. With the objective of representing vegetation communities across the network at a minimal cost, we ran scenarios with a range of representation targets (10%, 30%, 50%, and 70%) and used 3 approaches to account for connectivity (boundary length modifier, Euclidean distance, and landscape‐value [LV]). We found that decisions regarding the target and connectivity approach used affected the spatial allocation of reserve systems. At targets ≥50%, networks designed with the Euclidean distance and LV approaches consisted of a greater number of small reserves. Hence, by maximizing both representation and connectivity, these networks compromised on larger contiguous areas. However, targets this high are rarely used in real‐world conservation planning. Approaches for incorporating connectivity into the planning of linear reserve networks that account for both the spatial arrangement of reserves and the characteristics of the intervening matrix highlight important sections that link the landscape and that may otherwise be overlooked. El Efecto de la Planeación para la Conectividad en Redes de Reservas Lineales 相似文献
34.
Shie‐Yui Liong Chandrasekaran Sivapragasam 《Journal of the American Water Resources Association》2002,38(1):173-186
ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool. 相似文献
35.
DEVELOPMENT OF WATERSIIED MODELS FOR TWO SIERRA NEVADA BASINS USING A GEOGRAPHIC INFORMATION SYSTEM1
Anne E. Jeton J. LaRue. Smith 《Journal of the American Water Resources Association》1993,29(6):923-932
ABSTRACT: Techniques were developed using vector and raster data in a geographic information system (GIS) to define the spatial variability of watershed characteristics in the north-central Sierra Nevada of California and Nevada and to assist in computing model input parameters. The U.S. Geological Survey's Precipitation-Runoff Modeling System, a physically based, distributed-parameter watershed model, simulates runoff for a basin by partitioning a watershed into areas that each have a homogeneous hydrologic response to precipitation or snowmelt. These land units, known as hydrologic-response units (HRU's), are characterized according to physical properties, such as altitude, slope, aspect, land cover, soils, and geology, and climate patterns. Digital data were used to develop a GIS data base and HRIJ classification for the American River and Carson River basins. The following criteria are used in delineating HRU's: (1) Data layers are hydrologically significant and have a resolution appropriate to the watershed's natural spatial variability, (2) the technique for delineating HRU's accommodates different classification criteria and is reproducible, and (3) HRU's are not limited by hydrographic-subbasin boundaries. HRU's so defined are spatially noncontiguous. The result is an objective, efficient methodology for characterizing a watershed and for delineating HRU's. Also, digital data can be analyzed and transformed to assist in defining parameters and in calibrating the model. 相似文献
36.
Sheng-Wei Fei 《International Journal of Green Energy》2020,17(10):583-590
ABSTRACT In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective. 相似文献
37.
皖南山区是我国著名的南方亚热带丘陵山地的重要组成部分。从自然生态环境、社会经济条件、发展基础、科技支撑、发展资金等方面对本区特色农业发展条件进行了深入分析,并指出了皖南山区特色农业的发展必须坚持以市场为导向、发挥比较优势、依靠科技进步、尊重农民意愿、可持续发展的原则,以及皖南山区特色农业的发展重点是茶叶、蚕桑、干鲜果、中药材、蜂产品等的生产与基地建设。从合理布局与科学规划、开拓资金来源渠道、培育和完善市场体系、科技兴农、建立特色农产品原产地保护制度和进行组织创新等方面提出了皖南山区特色农业发展的对策。 相似文献
38.
39.
发动机结构日益复杂,其故障具有多样性和频发性的特点,收集大量故障样本存在很多实施障碍。为了提高车辆发动机的故障识别的效率和准确性,提出了一种新的结合故障树(FTA)和支持向量机(SVM)各自特点,从故障模式分析到故障类型识别的FTA-SVM故障识别方法。首先利用故障树在复杂系统故障模式分析中的优势,找出系统的故障模式,建立故障树模型,通过对故障树模型中各故障事件的分析,采集与故障事件状态相关的数据,建立数据与故障树底事件的映射模型,最后利用支持向量机在小样本数据处理中的优势,进行故障类型的识别。以发动机的失火故障为例建立了发动机失火故障树模型及故障数据与故障模式映射模型,验证了FTA-SVM方法的有效性和适用性。 相似文献
40.
为了解决周期来压的预测问题,首先对已知支架周期来压荷载曲线使用多重差异进化算法(MDE)进行拟合,将每重拟合形成的单一正弦曲线与上次差余曲线(Ei)再作差余曲线(Ei+1)。将这些Ei图通过分形几何的盒子法计算维度和相关系数(r)。将每条Ei的维度、r和支架相对距离(L)作为输入值,对应的Ei的周期Ti、缩放系数Si和纵移系数Di作为目标值,使用支持向量机(SVM)进行训练。通过对维度和r规律的研究得到拟设置支架处荷载各Ei的维度和r,带入训练后的SVM模拟得到Ei的Ti、Si和Di,进而得到Ei的表达式。将上述Ei求和即为所求拟设置支架处的周期来压荷载。实例分析说明,该种方法预测结果可以大体反映支架周期来压的基本形式和变化规律。 相似文献